{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 题目\n",
    "某餐饮企业的增长陷入迟滞，同时不同菜品口味和不同经营模式的餐馆更是层出不穷。在内忧外患的情况下，该餐饮企业希望结合餐饮行业现状，分析客户和订单的数据，挖掘数据中的信息，通过对客户流失进行预测寻找到相应对策，从而提高利润。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 导出依赖库\n",
    "导入pandas、matplotlib、seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取数据\n",
    "读取user_loss.csv与info_new .csv，存储为users和info，打印数据的维度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "客户信息表的维度：\n",
      " (2431, 36)\n",
      "订单详情表的维度：\n",
      " (6611, 21)\n"
     ]
    }
   ],
   "source": [
    "users = pd.read_csv(\"../data/user_loss.csv\", encoding='gbk')\n",
    "info = pd.read_csv(\"../data/info_new .csv\", encoding='utf-8')\n",
    "print(\"客户信息表的维度：\\n\", users.shape)\n",
    "print(\"订单详情表的维度：\\n\", info.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在这里可以看出数据中有2431条用户信息；6611条订单信息 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 浏览客户信息表信息\n",
    "使用info()方法查看两个数据的信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2431 entries, 0 to 2430\n",
      "Data columns (total 36 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   USER_ID         2431 non-null   int64  \n",
      " 1   MYID            0 non-null      float64\n",
      " 2   ACCOUNT         2431 non-null   object \n",
      " 3   NAME            2431 non-null   object \n",
      " 4   ORGANIZE_ID     2431 non-null   int64  \n",
      " 5   ORGANIZE_NAME   2431 non-null   object \n",
      " 6   DUTY_ID         0 non-null      float64\n",
      " 7   TITLE_ID        0 non-null      float64\n",
      " 8   PASSWORD        2431 non-null   object \n",
      " 9   EMAIL           0 non-null      float64\n",
      " 10  LANG            0 non-null      float64\n",
      " 11  THEME           0 non-null      float64\n",
      " 12  FIRST_VISIT     310 non-null    object \n",
      " 13  PREVIOUS_VISIT  0 non-null      float64\n",
      " 14  LAST_VISITS     289 non-null    object \n",
      " 15  LOGIN_COUNT     0 non-null      float64\n",
      " 16  ISEMPLOYEE      0 non-null      float64\n",
      " 17  STATUS          0 non-null      float64\n",
      " 18  IP              0 non-null      float64\n",
      " 19  DESCRIPTION     0 non-null      float64\n",
      " 20  QUESTION_ID     0 non-null      float64\n",
      " 21  ANSWER          0 non-null      float64\n",
      " 22  ISONLINE        0 non-null      float64\n",
      " 23  CREATED         2431 non-null   object \n",
      " 24  LASTMOD         0 non-null      float64\n",
      " 25  CREATER         2045 non-null   float64\n",
      " 26  MODIFYER        0 non-null      float64\n",
      " 27  TEL             2431 non-null   int64  \n",
      " 28  STUNO           2431 non-null   int64  \n",
      " 29  QQ              0 non-null      float64\n",
      " 30  WEIXIN          0 non-null      float64\n",
      " 31  SEX             2431 non-null   object \n",
      " 32  POO             2429 non-null   object \n",
      " 33  ADDRESS         2429 non-null   object \n",
      " 34  AGE             2431 non-null   int64  \n",
      " 35  TYPE            2431 non-null   object \n",
      "dtypes: float64(20), int64(5), object(11)\n",
      "memory usage: 683.8+ KB\n"
     ]
    }
   ],
   "source": [
    "users.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6611 entries, 0 to 6610\n",
      "Data columns (total 21 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   info_id             6611 non-null   int64  \n",
      " 1   emp_id              6611 non-null   int64  \n",
      " 2   number_consumers    6611 non-null   int64  \n",
      " 3   mode                0 non-null      float64\n",
      " 4   dining_table_id     6611 non-null   int64  \n",
      " 5   dining_table_name   6611 non-null   int64  \n",
      " 6   expenditure         6611 non-null   int64  \n",
      " 7   dishes_count        6611 non-null   int64  \n",
      " 8   accounts_payable    6611 non-null   int64  \n",
      " 9   use_start_time      6611 non-null   object \n",
      " 10  check_closed        0 non-null      float64\n",
      " 11  lock_time           6611 non-null   object \n",
      " 12  cashier_id          0 non-null      float64\n",
      " 13  pc_id               0 non-null      float64\n",
      " 14  order_number        0 non-null      float64\n",
      " 15  org_id              6611 non-null   int64  \n",
      " 16  print_doc_bill_num  0 non-null      float64\n",
      " 17  lock_table_info     0 non-null      float64\n",
      " 18  order_status        6611 non-null   int64  \n",
      " 19  phone               6611 non-null   int64  \n",
      " 20  name                6611 non-null   object \n",
      "dtypes: float64(7), int64(11), object(3)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "info.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 将时间转换为时间格式\n",
    "将users数据的'CREATED'列转换为时间格式;\n",
    "将info数据的'use_start_time'和'ock_time'转换为时间格式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2015/8/17 20:09\n",
       "1       2014/10/31 12:06\n",
       "2         2014/6/3 12:19\n",
       "3        2015/4/25 19:03\n",
       "4        2015/11/8 12:34\n",
       "              ...       \n",
       "2426      2014/8/9 20:32\n",
       "2427     2014/9/17 19:36\n",
       "2428     2014/2/21 12:05\n",
       "2429     2015/4/21 11:08\n",
       "2430     2014/7/27 13:45\n",
       "Name: CREATED, Length: 2431, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users['CREATED']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "users['CREATED'] = pd.to_datetime(users['CREATED'])\n",
    "info['use_start_time'] = pd.to_datetime(info['use_start_time'])\n",
    "info['lock_time'] = pd.to_datetime(info['lock_time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2015-08-17 20:09:00\n",
       "1      2014-10-31 12:06:00\n",
       "2      2014-06-03 12:19:00\n",
       "3      2015-04-25 19:03:00\n",
       "4      2015-11-08 12:34:00\n",
       "               ...        \n",
       "2426   2014-08-09 20:32:00\n",
       "2427   2014-09-17 19:36:00\n",
       "2428   2014-02-21 12:05:00\n",
       "2429   2015-04-21 11:08:00\n",
       "2430   2014-07-27 13:45:00\n",
       "Name: CREATED, Length: 2431, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users['CREATED']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 客户流失与年龄的关系\n",
    "以年龄为横坐标，客户流失人数为纵坐标，以users的'TYPE'列为区分，分别绘制客户流失与年龄之间的关系。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20     7\n",
       "21    24\n",
       "22    17\n",
       "23    13\n",
       "24    15\n",
       "25    11\n",
       "26    22\n",
       "27    18\n",
       "28    11\n",
       "29    20\n",
       "30    14\n",
       "31    14\n",
       "32    14\n",
       "33    16\n",
       "34    19\n",
       "35    14\n",
       "36    18\n",
       "37    11\n",
       "38    16\n",
       "39    20\n",
       "40    19\n",
       "41    14\n",
       "42    14\n",
       "43     7\n",
       "44    14\n",
       "45    16\n",
       "46     9\n",
       "47    13\n",
       "48    10\n",
       "49    13\n",
       "50     9\n",
       "51     1\n",
       "52     3\n",
       "53     3\n",
       "54     2\n",
       "55     1\n",
       "56     2\n",
       "57     4\n",
       "58     3\n",
       "59     6\n",
       "60     5\n",
       "Name: AGE, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = users.loc[users['TYPE'] == '已流失', ['AGE', 'TYPE']]['AGE'].value_counts().sort_index()\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20    11\n",
       "21    29\n",
       "22    32\n",
       "23    25\n",
       "24    20\n",
       "25    33\n",
       "26    19\n",
       "27    34\n",
       "28    30\n",
       "29    29\n",
       "30    26\n",
       "31    25\n",
       "32    25\n",
       "33    33\n",
       "34    21\n",
       "35    26\n",
       "36    26\n",
       "37    31\n",
       "38    30\n",
       "39    30\n",
       "40    28\n",
       "41    14\n",
       "42    13\n",
       "43    17\n",
       "44    20\n",
       "45    20\n",
       "46    20\n",
       "47    20\n",
       "48    14\n",
       "49    17\n",
       "50    20\n",
       "51    11\n",
       "52     2\n",
       "53     8\n",
       "54     3\n",
       "55     8\n",
       "56     8\n",
       "57     8\n",
       "58    10\n",
       "59     6\n",
       "60     2\n",
       "Name: AGE, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = users.loc[users['TYPE'] == '非流失', ['AGE', 'TYPE']]['AGE'].value_counts().sort_index()\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20    25\n",
       "21    54\n",
       "22    49\n",
       "23    48\n",
       "24    33\n",
       "25    43\n",
       "26    44\n",
       "27    40\n",
       "28    43\n",
       "29    36\n",
       "30    41\n",
       "31    37\n",
       "32    37\n",
       "33    37\n",
       "34    34\n",
       "35    36\n",
       "36    34\n",
       "37    51\n",
       "38    38\n",
       "39    42\n",
       "40    30\n",
       "41    19\n",
       "42    23\n",
       "43    22\n",
       "44    29\n",
       "45    22\n",
       "46    28\n",
       "47    27\n",
       "48    26\n",
       "49    23\n",
       "50    22\n",
       "51    10\n",
       "52    11\n",
       "53     8\n",
       "54     9\n",
       "55     5\n",
       "56     5\n",
       "57     4\n",
       "58     6\n",
       "59     8\n",
       "60     6\n",
       "Name: AGE, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = users.loc[users['TYPE'] == \"准流失\", ['AGE', 'TYPE']]['AGE'].value_counts().sort_index()\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>已流失</th>\n",
       "      <th>未流失</th>\n",
       "      <th>准流失</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>24</td>\n",
       "      <td>29</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>17</td>\n",
       "      <td>32</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>13</td>\n",
       "      <td>25</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>11</td>\n",
       "      <td>33</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>22</td>\n",
       "      <td>19</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>18</td>\n",
       "      <td>34</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>11</td>\n",
       "      <td>30</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>20</td>\n",
       "      <td>29</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>14</td>\n",
       "      <td>26</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>14</td>\n",
       "      <td>25</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>14</td>\n",
       "      <td>25</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>16</td>\n",
       "      <td>33</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>14</td>\n",
       "      <td>26</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>18</td>\n",
       "      <td>26</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>11</td>\n",
       "      <td>31</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>16</td>\n",
       "      <td>30</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>19</td>\n",
       "      <td>28</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>9</td>\n",
       "      <td>20</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>13</td>\n",
       "      <td>20</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>9</td>\n",
       "      <td>20</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    已流失  未流失  准流失\n",
       "20    7   11   25\n",
       "21   24   29   54\n",
       "22   17   32   49\n",
       "23   13   25   48\n",
       "24   15   20   33\n",
       "25   11   33   43\n",
       "26   22   19   44\n",
       "27   18   34   40\n",
       "28   11   30   43\n",
       "29   20   29   36\n",
       "30   14   26   41\n",
       "31   14   25   37\n",
       "32   14   25   37\n",
       "33   16   33   37\n",
       "34   19   21   34\n",
       "35   14   26   36\n",
       "36   18   26   34\n",
       "37   11   31   51\n",
       "38   16   30   38\n",
       "39   20   30   42\n",
       "40   19   28   30\n",
       "41   14   14   19\n",
       "42   14   13   23\n",
       "43    7   17   22\n",
       "44   14   20   29\n",
       "45   16   20   22\n",
       "46    9   20   28\n",
       "47   13   20   27\n",
       "48   10   14   26\n",
       "49   13   17   23\n",
       "50    9   20   22\n",
       "51    1   11   10\n",
       "52    3    2   11\n",
       "53    3    8    8\n",
       "54    2    3    9\n",
       "55    1    8    5\n",
       "56    2    8    5\n",
       "57    4    8    4\n",
       "58    3   10    6\n",
       "59    6    6    8\n",
       "60    5    2    6"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'已流失': a.values, '未流失': b.values, '准流失': c.values}, index=range(20, 61, 1))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '年龄与客户流失人数的关系')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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evoKlUygUCoWlEEIUGjctpeSNN94gNjaWTz/9lGeffZYVK1bQuXPn69rFx8ezYsUKcnNzWbJkCT169KB///7cfvvtdOzYkd9//51JkyYxYMAADh48aBGZ1crayMubXiYzN5N/Bv9z9VgH/w7Y6ezIk3nYYXrCdYVCoVBUXnr27MkTTzzBO++8c93x1NRUWrRoQVZWFnfffTdRUVG8//77LF++nA8++IDc3Fyys7MZMGAAkyZNIjU1lTVr1jB9+nTq169/dZymTZuyefNmVq5cScOGDS0is5BSWmQgSxIeHi4jIiLKdc6N0RvRG/T0rdP3uuNSSladXUW3oG642ruWq0wKhUJR3Th69ChNmzataDFMIisri4yMDLy9vS0+dmG/ByHEbilloY5SahvcSH2P+nQN6nrT8ZOXT/L8hudZELmgAqRS1EQ2x2zmm73fVLQYCkWNx9HR0SqKujQoZQ3oDXoGzR/EH4f+uOlcI69G/Hbrb9zR5I7yF0xRI/lqz1f8c/SfkhsqFIoag7JZA0h4v9v7NPZuXOjpDgEd+PPwnyRlJjE5fHI5C6eoaXzZ+0vWn1vPhfQLBLkGVbQ4CoWiEmCVlbUQwlYIcU4I8Z/x1VII8asQYpsQ4nVrzFkW9AY94QHhNPBsUGSb2CuxnEs7h0EaylEyRU1Db9Cz5uwaPt71MavOrKpocRQKRSXBWtvgrYAZUspeUspeQBhgI6XsAtQXQoRZad5SEXExgoHzBnIs6ViRbZ4Pf54ve39JfEZ8OUqmqGmcTzvPlIgpDG8wnFFhoypaHIVCYaSiS2RaS1l3BoYKIXYKIX4F+gGzjedWATeljRFCPCyEiBBCRCQkJFhJrMIJ8wzj3a7vFlu4w1Zny8bojQycN5C98XvLUTpFTaKOWx0Wj1zMHU3vYGP0xooWR6Go9rz22mssW7bspuNPP/00J06cICcnByklH3744dXMZXq9HoPh2i7ryZMnMRgM/PPPP3zxxRdWkdNaNutdQD8pZawQ4i+gD/CT8Vwy0O7GDlLKqcBU0EK3rCRXodjZ2NElqAseDh7FtusQ0IH7WtxHqHtoOUmmqGkcSDhAYmYiF9Iv8Nnuz+ga1BUfJ5+KFkuhqFa8/vrr7Nq1CyEEkZGRrFy5kq+//prs7GyeeuopRo0aVWNKZB6QUmYb30cAowAn42dXKpkX+k/7f2Lp6aVsvX1rse2cbJ14ut3TbL2wlazcLPrU6VNOEipqCnNOzCHiYgQzhsygT50+eDtWjrARhcJq/H5z5SsA7tNWsSx/GeIKyQI28H8Q2Ar2/gP7pt/crxjef//9q+/ffvttOnfuzMCBA68eq0klMqcJIT4ADgEjgSfQtr63A62B41aat1SMbDiS9v7tTWqbZ8jj012fEuYVRp86fVh5ZiUdAzri5ehlZSkVNYE3u7xJYkYivk6+rI1fS3RaNF2Db47/VygUpSM3NxcAW9vC1V9OTk6NKpH5LjAdEMBiYCGwSQgRBAxCs2lXGlztXekY2NGktjY6G36/9XfS9GnEpsfy/IbneartU9zb4l42Rm+kR3AP7G3srSyxojoipWT28dl0C+oGwLf7viXINUgpa0X1pqSV8KCPij/f9k7tZSLLly/n22+/vZoffOfOnTg5OdGzZ09As0f/9NNPla5EJlLKcnkBXsA4IKCktu3bt5flyaB5g+QL/71Qqr7Hko7JxIxEueH8BtnijxZy/bn1MiEjQe6L3ycNBoOFJVVUZ2LTY2WLP1rIWcdmSSmljEmLkdm52RUslUJhWY4cOVLRIkgppdTr9XLo0KFy+PDh8pZbbpG//PLLTedfeuklKaWUffv2la+99prcvn27HDx4cKHj5eTkyPDwcNm5c2cZHx9f4vyF/R6ACFmEXiw327GU8pKUcraUMq685jSVlzq8xO1Nby9V38bejfFx8qFrUFd+6PcD3YK7sTByIXctu4uY9BgLS6qozgS4BLB5wmYG1xsMgE7o+OvIX1y8crGCJVMoqhfp6emMGTOGMWPG0LZtW1544QUWL17MlClTyMvLA2DmzJmlKpH51VdfMWzYMPbutWzUUKVy9KoI9AY9AS4BNPYqPHuZqdjqbOke3B07nR0TGk/gq95fkZSVxOcRn6tEKgqT2BW3i+Wnl+Nkq/liJmcl89WerziSdKSCJVMoqg/btm2jW7du3Hnnndxzzz0A2NjYMHv2bGJiYmjatCmbNm2qdCUya7yyjkuPY8ySMaw5t8ZiY7rau9KnTh9OXDrBjGMzSMgo37hxRdVk1ZlVfLvvW3RC+7ds5NWIzRM207tO7wqWTKGoPrRt25bFixczduxYQFtl5+bm4uDgwBdffMG6detwdna+rkTmI488gl6vv65EZnR09HUlMu+7776rc+SXyJw8ebIqkWkp0nPS2XphKy18W1g8D3NOXg42wgYbnY1Fx1VUT6SUXM6+fF1kwbpz64hKieLBlg9WoGQKheVQJTI1VIlMM8kx5NDAswG1nGtZfGw7nR1TD0xlxZkVFh9bUf34fv/3nE09e92xHbE7mHN8DpXxoVqhqO6oEpmViJVnVjJy0UhSslMsPrYQguVnlrPn4h6Lj62oXqTmpPLrwV/Zn7D/uuOTwyez4rYVhTq1KBSKmkONL5HZPbg7n/b8FE8HT6uMP3/4fGx1Nf7XrCgBd3t3dt65k1xD7nXHDdLAlIgpdArsRM/aPStIOoVCUdHU+JV1niGP5j7NrWZXPpR4iJc3vUyGPsMq4yuqB9tjtzP1wNSbjjvYOLD89HJOXDpRAVIpFIrKQo1X1p9GfMrkDZOtNn5yVjK74nYRl1HpwssVlYh98fv48/CfN2W/0wkda8auUQ5mCoWV2LZtG+np6VcrahVFdS2RWWV4tNWjPB/+vNXG7x3Sm7Vj11Lfo77V5lBUfR5t/SibJ2y+GrZVkIi4CJ5c9yRZuVkVIJlCUb04dOgQU6dqu1gGg4F7772X7Oxs1q9fz8KFC6+2qyklMqsMEkkDzwZWG18IwecRn+Pr5MvE5pZP7q6oHny661M6BnTklpBbbjp3RX+Fs6lnSchIIMQ9pAKkUyiqD02bNuWNN97A09MTOzs7EhMTufPOO8nOzubAgQN89tlnfPvtt5WuRGaNXlkbpIF7lt/DP0f/seo8p1JOEZ0ebdU5FFUXfZ6eFadXcCz5WKHne9fpzeKRi5WiVlRL7ltxHwsjF1r0fXHY2Ngwbdo0/P39efPNNxk2bBgrVqzA39+fqVOnsmnTJtzd3a+WyPzll1/o2rUrn3zyCdOnT6dHjx6sXr2adu3aAdeXyPzoo4/4/vvvGThwIH379uXbb7+10G+phq+spZR81/c7AlwCrDrPd32/s+r4iqqNnY0da8etLTYt7ZRdU7DV2fJM+2fKTzCFopri6uqKnZ0dYWFhrF69moEDB7J//34mTpyInZ0d06ZNq3QlMmt0BrO0nDSiUqII8wzD2c7ZavMcSDjA9/u+560ubxHoGmi1eRRVk+2x21lzdg1PtXsKd3v3Qtu8vfVtbHW2vN759XKWTqGwLJUhg9nRo0fZt28fbdu25aOPPuKPP/5gwoQJfPTRR9StWxeA7OzsqyUynZ2dbyqR+dFHH7Fnzx6eeeYZXFxcgGslMoODg4HiS2Sam8GsRq+sDycd5qFVD/H7rb8THlDo78ci6ISO5KxkLmdfVspacRNnU86y8sxKXurwUpFt3u76dvkJpFBUc37//Xf69esHwLp16xg5ciQRERE8+OCD2NnZsXz5cmxsbJgyZQofffQR/fr1o3PnzgwbNox33333qqNZ+/bt2bRpE6A5nXXt2hVbW1umTZuGn5+fRWWu0TbrJl5N+K7vdzTybmTVeVr4tmD2sNk09Smfp8l98fv4cMeHxF1R4WJVgfFNxrNx/EbsbOyKbHMm5Qx3LruTnbE7y1EyhaL6kZyczOrVq68q6z59+rBw4UK6d+/OL7/8wvLlywFVIrNSkZaThrejN252blaf6+OdH/Pxzo+tNn50WjT/2/E/krOSiUmPYdnpZao0ZxXh3W3vsvjU4mLbeDt5YytsyZN55SSVQlE92bhxI3feeSc6nQ4pJcuXL6d79+5s3LiRsWPH0qlTJ7777rtKVyKzRm+Dzzo+i1nHZ7HzTuuvViQSgWXzO6fnpDPz+Ey6BnXFVmfL3BNz6R7cnQF1BzCg7gBi0mJYfXY1/UP7W3ReheWQUnIk6Qh+TsVvmbnbu/PnoD/LSSqFovoycuTIq+/z8vIYNGgQf/zxx3Vtdu/eTWZm5tUSmcuXL+eDDz64rkTmpEmTriuRWb/+tVwa+SUyV65cqUpkWoLzqec5n36erkFdrT6XpTBIA/9G/Yungyft/dvTe3ZvHm39KPe3uJ/0nHRc7V2vtn1m/TMcTDzI8tHLb8qMpah6/HHoD5ZELWHe8HkVLYpCUWoqg4OZqagSmZWEy9mXCXQpH4evI0lHGLdkHIeTDpdpHIHgt4O/sShyES52Lqy6bRX3t7gf4DpFDfBap9eYMWQGoOVALy3pOek8vuZx5pyYU3rBFYWyI3YHk/+bTEJGQolt/V38ae7TnJy8nHKQTKGwHpVxkVgY1iqRWZrrr9Hb4G9ufZNQ91C+7P2l1efydPDE29G7TEpzwckFZOVl8fOAn/F18tXGdfQssr2fsx+pOancvvR2BtcbzAMtHyjVvBczLrLlwhZe7PBiqforiuZy9mWOJx83KXRwUL1BDKo3SPkiKKo0jo6OJCUl4ePjUyNLv0opSUpKwtHR0ax+NXob/HDSYWyFLY29G1t9Lkvw9LqnSden8+utv5rcR0rJBzs+oEdwj0JTWRZHniGPPw7/wYQmE8iTefx76l/CvMLoENDBXNEVFsAgDYxeNJo+dfrwVLunKlochaJU6PV6oqOjycqqubnuHR0dqV27NnZ210eAqDjrQsg15JKSlWL1sK2CfLjjQ2LTY/mm7zel6v9Vn6/IzM00q48Qgtc7v06GPoNfDv7CPc3uKTZEqCAHEw/yzd5v8HP2Y3C9wfxy8BdurXurUtYW5LXNrxHmGca9Le4tsa1O6Oge3J0wrzDrC6ZQWAk7Ozvq1atX0WJUOWqszTopM4lH1jzC+vPry23OYNdg6nmU7o/0v/P/8fbWt8k15Jaqf8TFCL7a8xXbY7eb1H5X3C5a+7Vm3vB5DG8wHFudLXOGzeGljkUn7lCYT1pOmlkPYM93eJ6+dfqaZfPS5+nZc3EPF69cLI2ICoWiElBjlbWnoyd/DvyTW2qbtzVcFu5pfg+TwyeXyrkgJj2GnXE7cbFzKdXcPWv3ZOGIhfSo3YP0nPRi2+6K28X9K+9n0alF11Uk83Tw5L1t71m98ElN4us+X/NYm8dMbr/u3Do6T+/MmdQzJrU/n3qet7e9zT0r7mHV2VWllFKhUFQ0NVZZX866TI4hBzd76ydEyScqJYq+c/qyIXqD2X3vbHonS0ctLbTesak08GzA57s/565ld5Gdl11om0tZlwj3D+e9bu8xpP6Q687Z6Gy4cOUCSZlJpZZBcY1dcbu4b8V9nEs9Z3KfMM8w7mp2Fw42ppXhW39+PevPreftLm8zrP6w0oqqUCgqmBqrrDfHbOahVQ9xOetyuc3p7+xPh4AOeDp4mtUv8lIkE5dP5Pil42WWoXNAZ/qG9kVXyK0/kHCAgfMGsiF6AyMbjsROd7Nt+/u+3/NUu6fQG/RllqWmozfoyTXk3hRyVxwh7iE82+5Zk3ZY4q7EMbH5RBaPWky34G5sitlUZUJmFArF9dRYB7PedXpTx70Ovs6+5Tani50LH/X4yOzwrTR9Gjl5OWYr+cLoGtyVLkFd+DfqX2q71aZtrbaA5jXewLMBg+sPprVf6yL7CyF4cu2TuNhr11Ia8gx5JGclE3slFludLc18mpVqnKpO16CupUrI88TaJ0jJTuGfIUWbI6JSohi3ZByvdHyF2xrdxoxjM/hwx4e0929PkGtQWcRWKBQVQI1V1nFX4rDT2RW6erQmH+74kJ2xO1k4cqHJfdr4tWHm0JkWkyE7L5vv931P61qtaVurLTHpMTy7/lne7fYub3V5q8T+rWu1xtHGvBjBgiw7vYw3t75JriGXLoFdmDpgaqnHqsq8uOFF/F38eS78ObP6jWw4kqy84sNeglyCmNhs4tVwvVvr3kqnwE5Wr92uUCisg1WVtRDCH1ghpWwrhPgVaAYslVK+b815TeGXg79w6vIpFo1cVK7ztqvVDi8HryKrt9xIdl42wxYM4/E2jzOy4UiLyOBo68ivt/6Kv7M/KdkpZOdlkyfzsBE2JvV/sOWD6PP0RKdFU9utttnzt63Vlvua38etdW8lyDUIvUFf7g9NlQF3B3dc7UzfAs9nQN0BJGclk5aTVqjPxd9H/ibQJfC6WGxvR2+OJB3hbMpZetfpXSa5FQpF+WNtm/UUwEkIMRqwkVJ2AeoLISo8UPTZ9s/yvx7/K/d5B9YbyMOtHjbZ5puek06HgA4WT4sa5BrEwcSDdJ/ZHRthw5xhc8yK331i7RM8+9+zZs+76swqUnJSeKrdUzT2bkx8RjzDFgxjV9wus8eq6rze+XUeaf2I2f1i02O5ZdYtLD+9/KZzuYZclp9ezsqzK2869/uh3/n54M+lklWhUFQsVlPWQog+wBUgDugFzDaeWgV0L6T9w0KICCFEREJCyXmSy8rplNM42pZ+K7e0xF2Jo/P0ziw5tcSk9q72rnzQ/QM6BXayuCxNfZoysuFI4jPizfYyn9h8Ik+2fdIsh6U8Qx5f7/2a7/Z+d/VYoEsg9TzqlWlbvSqyL34fIxaO4GjSUbP7BrgE8GKHFwn3vz7RUU5eDomZifwx6I9CzRkfdP+A3wf+XmqZFQpFxWEVZS2EsAfeAF42HnIBYozvkwH/G/tIKadKKcOllOF+fsWXC7QEz294nrkn5lp9nhup5VyLCU0m0NDLtLJpdy+7mze3vGkVWRxsHHiv23ulykjWPbg7jbwasSlmk8l9dELHjCEzeLPLtetxtnPmh34/UNutNvNPzjdbjqqKjbAh1D0UL0cvs/sKIRjbaOxN4Xc/7v+R0YtGk5KdUqi3uJOtE9OPTud4ctmjChQKRflirZX1y8D3UsrLxs/pgJPxvasV5zUJKSXTBk3jzqZ3lvvcOqFjcvvJhLqFlthWSsmgeoPoHNi5xLYVwVd7vuLlTS+jzyt5S/9C+gXGLBnDmZQzhTo5zTg2g/e3v8+F9AvWELXS0dKvJV/3+brUDl+/HfqNCUsnkKHPuHpsVNgoHm396NUiL4Xxxe4viLho/bz7CoXCsljLwawf0EcI8QTQBqgDnAe2A62BCn20T9OnEZ0WTZtabSpk/k92fcKiU4vYMmFLsU5ml7IvcWfTOyttLerHWz/OE22eMCnX+KWsS9jqbItUJA+0fID+of0JdAkkOy/b5KQfVZXJ/03Gwcah1H4TA+sNpLF3Y2x0NmTmZvLOtnd4rPVjTGw+scg+Hg4ebBy/sdhKbQqFonJilRWulLKnlLKXlLIXsA9NYd8thPgcGAcstca8phJ5KZJn/numwrYD+9Tpw6Q2k8iVxef5/jzic4YsGFJpE1mEuIcQnxHPD/t+KLbdhfQL1HarzcwhMwl0LdxRzsHGgYaeDXlq/VO8vfVtK0hbuWjs1ZiGnqaZQgqjvkd96nnUIzotmlOXT7E5ZrNJub+jUqL4YPsHqsymQlHFsHqctVFhI4ToBfQHPpFSplh73uJo6tOU2UNnE+IWUiHzdwjoQBPvJmToM/Bw8Ciy3dAGQ2nv375S13zdE7+H6cemc0fTO4q8lne3v8v51PMsHrm42PAwIQSt/VrjbOtscmhbVURKWSov8Bt5fM3juNm7MX3wdFbettKkrGZnU8+y7PQyHmj5gIq5ViiqEDWynnXkpUii06PpHtwdW13554XJ0GfQeXpnnmz7JA+1eqjQNvlxtHXc6lRqpZWhz8BWZ1vsVv3x5ONEp0XTN7SvSWPmGfKYeXwmA+sOxMfJx1KiVhoOJx3msdWP8Vmvz8pUbnRzzGYeW/MY/UP783mvz03qozfosRE2Zcoxr1AorENx9axr5H/sstPLeGb9MxX2heVs58zLHV+mW3C3ItssjVrK0AVDibsSV46SmY+znTNbYrbw4MoHbyrfaZAGPt/9OY62jiYraoDo9Gg+i/iMRafKN2FNeeFk60SfOn3KvLLtHtydr3t/zb3N7zW5j53Ojt8O/cbvh1QIl0JRlaiR6UYnNptI/9D+Fbq6GNNoDOfTzhd5vl+dfng4eBRp461MSCSZuZkkZiZep4BOXT7FzGMzCfMMI9S9ZO/3fELdQ5kzbA71PepzRX+l1GVBKyuhbqG83fVti4xVmmxkhxMPm+QUqFAoKg81cht864WtONo40s6/ndXmKInv9n3H1ANT2XHHjpuSs+Tk5bA5ZjOdAjtVCUWVb1/Oycu5uh2ea8hFJ3QkZyXj7ehdqgejRZGL+Hz358weOht/l5tC86ssk/+bTGp2Kr/c+kuFzF+d/QEUiqqM2ga/ga/2fFXhaRcHhA7g4x4fF/qluT9hP0+vf7rKpOAUQrDqzCp6ze5FQoaWfW7GsRncsfQOHGwcSr2D0bZWW26pfQs5hhw+j/icw4mHSclOscj7iqRrUFd61u5ZYfPHZ8Tz9Lqn2RG7o8JkUCgU5lEjt8G/6v2VSYk8rElDz4Y42DiQmJlIsGvwdefa+LXht1t/o7lP8wqSznwaezemf2j/q3ZrP2c/Gno2LLTQhKnUca/Du93e5UL6BWYcm0GYVxhejl4WeR/iHoIOnVm1pC2BQRoY0WBEhW5Duzu4E5USRUp2hQZlKBQKM6hx2+AGaWDJqSW09mtNXY+6VpnDVDk6T+/M6LDRvNzx5evOzT85nzZ+bajvWb+CpCs9UZejSMpKoqVvywrJvW4KeYY87lh2B14OXvzQ74dy3RKOSonitkW3MeWWKWY53SkUiuqP2gYvQEp2Cq9veZ0tF7ZUqBw6oeN/Pf7HuEbjrjuempPKO9veYdXZVRUkWen57/x/jFg0gvtX3s9nEZ9VtDhFYqOzYWKzidzT/B4kkjxDXrnN7WzrzH0t7jM5N7y1WH56OeP/HW9y9TeFQlGx1LhtcHd7d5aOWlqm7VlLEe4fzpGkI9TzqHd1dedu787asWsRVD0HoE6BnXixw4sEuQTR1KdpRYtTLEPqDyHPkMerm1/F1c6V1zq9VqoVdn4t7qTMJNwd3Eusy+1u786ktpMqPM7Z0cYRH0cfUrJTis0lrlAoKgc1bmV9MeMiBxIPYKMrOpNWebHs9DIeXv0wFzOupYmcd2Iee+P3VslkIE62Ttzd7G76hvYlyDWoosUpERudDbWcapU63vlI0hGGLRjG4aTDPLXuKZ5cV3LJ0Pe2v8eIhSNKNZ8l6V2nN9/3+14paoWiilDjVta7L+7m1c2vsmTkEtzt3StUlt4hvWng0eC6Momzjs+itltt+of2r0DJag7Ptn8WIQSzjs3CRmfDmEZjSuyTa8glISOBQJdAQtxCsNPZMbH5RGyFLbmGXHbE7aB78E0l2wHoF9qPNn5tLHwVpeOFDS/gaOvIe93eq2hRFApFCdQ4Zd0/tD8tfFsQ7BZccmMrE+ASwMWMi0Rdjrq6bTxz6EzSctIqWLKagxACgzSwIXoDdjo7bgu7rcTt8Fc3vcqhpEMsGLGAnwdoIYCNvBoBMO3IND7Z9Qmzh86+yRQgpaSNXxu8Hb2tczFmUse9Do42ldMJUKFQXE+N8waPiIvgUvalSrNyHThvIK18W/HJLZ+w8sxKjiYdZVLbSRWSs7wmk5mbiZ3OjsNJh8k15NLev/1NbTZGb6RtrbacvHSS2CuxDKk/5KY2eoOejdEb6VunL38e/pPuwd1p4NkAgNj0WAbMG8A7Xd9hdNhoq1+TKUgpMUhDpTALKRQ1HeUNXoC5J+dWKk/lT3t+yuTwyYBmA11/fr1S1BWAk60TNsKGD3d8yCe7PrnJ9hyTHsPT657m90O/086/XaGKGrTc233r9CUlO4XfDv3G/JPzr55ztHXkxQ4v0rZWW6tei6mcTz1P79m9WXNuTUWLolAoSqDGraxTc1JJzU6ltlttq4xvLgkZCeyI20H/0P442DiQa8hVyroCiU2Pxc7GDoEgOy+bnLwcFp9azJNtn2R77Hba+7cvtsJYQRIzE3G3d2dTzCZWnl7JU+2ewsvRq9KkkNUb9Lyz9R1ua3RbpXmAUChqMsWtrGucVtgYvZHarrUrjbLeG7+XVza9Qmp2Kttit/FSh5fKV7bzu6BWU3Ao30xelZVA10CklNy57E70Bj2D6w1mzok5jGk0hi5BXcwaK9/TOjY9lpj0GH7c/yMbojewacIma4huNnY6O97v/j4Z+oyKFkWhUJRAjVPWH+38iIF1B9KmVpuKFgWALkFdmD98PqdTThN1OQoPB4/ymzwjGX4bAI0GwYR/QBV3ADSnsxc6vIBA0MqvFcMaDCtTiNNdze5iQpMJ7E/YX6b61dbgz8N/8tWer9h6+9ZKm3FOoVDUwG3w5KxkpJSVKo55c8xmdOjoGty1fCc+vRH+HKa9H/UTtJ5QvvMrKpwDCQfYHrud25vcXikSBSkUNRnlYGYkNSeV1WdWk5mbWdGiXMf/dvyPR9Y8wsbojeU78UVj9Sn/lnBiZfnOragUtPJrxX0t7iMnL6eiRVEoFMVQo5T1+dTzvL/jfU5eOlnRolzH651fJ9w/vOT424xkWP8hWKpiWGhX6PcO3PsvjPnNMmNWdnKztd/hlaSKlqTSMOHfCby97e2KFkOhUBRDjbJZN/Fuwrqx6yqNN24+XYK6mOa8FLMHNnwMweHQaEDZJw5srb3yObMZ0uKgZclZvKosKdHa71CfCQNU5i6AB1o8UO6lQhUKhXnUqJX18UvH+S/6v6qbACKojfYz8XjZx8rLhV2/QPLpa8c2fwGLn4LkqLKPX1nxaQAeIXD5XEVLUmkYVG8Q9TzqkZqTWtGiKBSKIqhRynpT9Cbe3fZulaxoBcDhBdrP2P1lHyv5FCx9Ds5tv3Zs2Fegs4WFT0A5lo0sN7LT4NB8CGgJ53dWtDSVhqiUKAbPH8z6c+srWhSFQlEENUpZ39/iflaPWW1yUosiWfWGZvcsT7JSYNnz2ntLKOuLh7Sf/s2vHfOoDYM+hnNbYfsPZZ+jsnFsGcy9D+ycIO2CtiWuoJ5HPd7o/EalCytTKBTXqFHK+r/o/ziceLjsA2WnanbPjOSyj2UqF/ZqP0M6Q+JJyE4v23hxh7RVtF/j64+3ngCNh8DadyHBAtvtlYnD88E9GLpM0j6f31Gx8lQSdELHwHoDuZB+oaJFUSgURVCjlPW0I9OYfmx66Qc4OBf0WdDhQePnOZYRzBRi9mg/Oz0Mod0gs4wPChcPg28jsHW4/rgQMOxLzbabHl+2OSoTmZcgci00HwUBrTSF7d2goqWqNEw/Op37V96v7NYKRSWlRnmD/zzg59KnVtz+I6x4CW79ELo8AYFtYM9f0PHh8sn8FbMbvOtDi9u0V1m5eFgL3SoM11rw6BbQVaNnuWNLwaCH5qPBxhZu/aCiJSqe3BywLaO5xgyG1B9C21ptcbJxKrc5FQqF6VSjb+OSmXF0BjHpMeZ3PDgXVrwMTYZCp0e1Y+3u1uy++dvT1iZmDwQbyzYaDJCeUPqxDAZocwc0G150G51O2+af+4BlbOQVzaH54BkKwe20z+kJsH8W5FypWLmK4veBMG0UHJgDOdbP3R3iFoKrvStHko9YfS6FQmE+VlPWQghvIUR/IUTpkypbkAx9Bp/t/oydcWZ6AZ9aDwse1Vaht/0K+WFfLcaArRNElYMHrSEPuj4JrYzpQGfdqX2RlxadDvq8Bk2Hldz2zCbt+nOzSz9fZaDDA9D3zWu7IBf2woKHr5kXKhN5udCgLyRFwvwHYUqY5qF/ehNYMT3wO1vf4du931ptfIVCUXqskhtcCOEFLDW+JgB9gI+AZsBSKeX7xfW3Vm7w/C1wZztn0zpcPAK/9tdWZPctAyfP68+nxoJ7oGWFNIU178DWr+HVCzfbnE0h/qi2WgtuV/IW/olVMH0sdH8W+r1dKnErJRnJ8Ek96PMG9Hy+oqW5xqo3oNkIqB2u7YCc2wb7Z8DhheDiC0/t1e5ZSgx4BFt06qNJR/F29Mbfxd+i4yoUCtOoiBKZrYDJUsrtRsXdB7CRUnYRQvwmhAiTUpZrzs/zaedZe3YtQ+oPMV1Ze9fTvKN7PH+zooZrijorFRzdLSbrTZzZrK2u69+ifQ5sDYZciD8CQaWoQ7ztOzi+HF6ILLltowHQbiJs+Qpqd9AqdFU1W/bGKRDSEer1vHbM2Rt8G1eueOvItdpDmFugpqx1OqjbTXsN+gQun9UU9eVz8GUrzSzSZDDk/z27BWgOdHBz6J29K7QcC3ZFV9aq7VabDdEbaFerHUGuQVa6SIVCURqs8q0rpdxgVNQ9gY7ArcBs4+lVQPcb+wghHhZCRAghIhISymCPLYKjSUf5bPdnXMq+VHLjtDhIOKHF4w75rPjV8+yJMH285QQtjI1TYNVr1z4HttJ+ltaWfPGQFl9tqmPcgA/Aqy7MvAPObtGOVZVt8fR4WP+BVmHsRkI6QvRObQVb0RgMsPot8KyjbdnfiL2zVnccwMFNS5Wqz9RC7Fa8rL12TL3WfsUr146veBkWT4Jf+xV731JzUnll0yvlX1BGoVCUiNW8wYUQAhgPXAIkkO/ZlQy0u7G9lHIqMBW0bXBLy9M/tD9bb9+Kk20J3q5ZKfD3GMi6DE/uKdkjN6gtrHlbi332DbOUuNeQEi7subZiAvCqBw4epVPWhjxtGzy8EIVQFI7umnf4ieXXPMhn3aWFQ7WeoHlYO5dQhKSiOLIIpEGT8UZCOsHeaZpt2K9R+ctWkIOz4eJBGP1LyaYNJy/Nh6HLJC3mXxofNnQF/p1fOn19n3M7tDS1tg7X7N43PKwFuQQxd9hcGno2LOPFKBQKS1PiylpoFLqHLoSoV1Q/qfEEcADoCuRrSVdT5rU02y5sY8mpJdjqink+0WfBzDsh4agWa2xK6EzrO0DYaF/61iA5SnuAyPcEB+1LNrTLtS9pc0g6BblZENDCvH72zlrIWL6DXf1eWmKWpc/BZ4015X1sqeYcVZk4NB/8moB/s5vP1e2ueffb2JW/XAXRZ8G69zXzhjlheUKAo4emvJ28tBV3PvnH8l+NB0K3p7VzO36E+Q9p6VevG04ghODngz9TGevcKxQ1GVOV5iIhxOdCiElCiA4AQoj2QKEZRoQQLwkhJho/eqI5l+VvfbcGzpRa4lKy6uwqfj34a/GNljyteT+P/AEa9jNtYDd/aDQQ9k23XOnKgsTs1n4G3bAZcccsLZe3uRSWZrQ0dHkCHt8Gj2zUksSc3QYLHwdZiXKKp17QHLQKW1WD5pMw6GPtZ0WScBRy0qH/u+XjD5CbBYfmwU89bwo9PJR4iB/3/1i6EMdyYEfsDn7YVw1T4SoUJVDsN4MQQkjtETsS+Ak4AQwSQuwBvgXGFtF1KnC3EGIjYAMsNH7+HBiH5iVerrzV5S0WjVxUdIO8XO0LLPwBaDXOvMHb3Q1XEuDEyrIJWRgxuzUHIr8mN5+T0vwHBEcPCBtQ+HjmIoS2Ghz4P3juGNy3XNtmTbuoxaZXNIcXAhJaFKGsQVPoR/8tL4kKJ6gtPHNI260oD7o/C/cu1ezXv/SHbd9f3RofWHcgW27fQm232uUji5n8fPBnvt//PTl5ORUtikJRrpT0GL9cCDEX8AAaAp2BcGAxEA34FdZJSnlJStlfStlTSvm4lDIF6AVsB3obP5crM4/PJOJiMeFgV+I1RRZ8kzm9ZBr211KAWmNVGdJRs0/a3LB9f/kcfBRqvlJs2BfunFO6kK/isLG7ttW8aQrMe7DiFXa7u2HCjOJ9CfZN1+LWyzPPe0FOrNTmdijnetKhXeHRzRDWH1a+ovldoIU1rju3jmlHrGTWKQN5hjx+GfAL+yfuJ9dQycwtCoWVKcnBbAwQCkwC3gVigRFSyjwhRF3gNyFEX2mCgUtKeYlrHuHlzm+HfqNzYGd6hfQqvIF7ELx4qnSewTa2Why2NSjKhukeDHk5mpNZm9tNH+/iES3vt6WVdUH6v6sVClnwKDj7QIPe1purOBzctNCm4qjTWfsZvQsa3Wp9mQpy+TzMulu7f6UxaZQVZ2+YMF2ra55v9snNYXPMZqLTorm72d3lL1MxfLLrE86knqFdrXZMPzadNWPWYFfR/gYKRTlR0sr6ceAZ4DKao9hrwGkhxP+Ab4CXTFHUlYGVt63k1U6vltywLDbDS2e0mGhLkRanhRzps24+p7PR6jLHHTB9vMxL8EMX2P695WQsDDsnuH2GVihk1l3ll5K1IBG/waJJJTu8BbXTHAQrIt56vTE/eY8KTMoiBHR8SLPb52TA1F68EzKEf4b8U3EyFUGoeyhNvJvQzr8d4xqPI8dgua3w7/d9z56LlTCbnUJhpCTN5I4WdtUVsEdbie8H9gF1AQvUm7Q+6TnpfL/ve86lniu60YpX4I+hZZto8ZOw6AnLxe0eXwZ/DoO02MLPB7aG2AOmz3fRmPfZv6Vl5CsOJ0+4ax44eWuhcGlx1p+zILv/1IqV3Gg+uBF7Zy1uvbzLZcYdhP0zodMj4BlSvnMXhT4T4g/DqfW8v/191pxdU9ESXSU+I56xjcfybPtn6RDQgUdbPcrplNMldyyBXw/+ysbojUw/Np298XvR5+n5es/XnE89bwGpFQrLUZKyXgOcBtoCx4FRaN7cA4FPABOWqhVPfEY8Uw9M5UzqmaIbxR0se6KPtncbV9ebyjZOPjG7NWXnVbfw84GtICcNLpn4pWUpT3BTcQ+Eu+dD50fBtRxTWCadgth9xTuWFSSkk/a7Ls+wszVvaz4SPSaX35wl4eIDfk1xuHiYHbE7OJt6tqIlusqrm17lvhX3Xf38x+E/uGPpHcSmF/EgawJX9FeYdmQa++L3sW7sOm5vcjtHk4/y+6HfOZ16mrgrcfx1+C9SssvdxUahuImSbNa9gVQ0z+/GaHbrrsB2KeU0IcQMIYROytIE/JYf9T3rs+fuPUiK2bFPPKk5X5WFpsO0ZCV7p11LDVoWYvYWn787sDXYOmoPCD4m1Ga+eEhT/m4BZZfNVHzDoOcL2vszm7Va0tZMzQpweIH2s2AimeJodKvmDa2/AjYe1pMrn/ijELkGBryvxUBXJgJbIU5vZPHko4jyKP1qInc1u+s6D/Ah9YdQ2602Pk4+pR7Txc6FFbetINeQi72NPfY29rTya8W6cetwtXdlceRiPo34lD51+nA+7TzRadH0De2LnU7ZyRXlT7EraynlW8CPaMp6MpAOvC6l/EkI4Qd8XdkVNcDBhIP8fPBnsotaOWelQnqcaQqvOOyctLCvI4s1+3BZyLmixd/eGF9dEP+W8EqM6Q8ZcYe0ZCgV8SWcngD/jNVSlhZmg7ckhxdoq2UPE8OPGvSBwZ9oK93yoFZTePg/6PBQ+cxnDoGtIS2WI9FbeHDlg8WbjsqJXXG7aOrdlAF1B1w9FuASQIeADsw/OZ88g/lRGJeyLjFm8RgOJh7E1f56T3wvRy/sdHbc1ug2Vty2gtputVkYuZD3tr+HlJKtMVuVfVtR7pToTSWlvCKlvCClvCiljJFSbjWemoKW87vScyDxAN/v+77olULyKe2njwXShba7G/KytTrEZSF2v5ahrGDmshvR6Uq2yRbELUALMasIXP00j+czm7TSlKX4gjWJlBhIOGb6qvpqv2gtJae1SYnRVvFBbYstqlFhBLYGwCn5DCk5Kabl0rciBmnglU2v8O72d286tzNuJx/s+ICDiQfNHjcxMxF7G3u8HYtPkxvsqlU2e6XjK/wz+B/sbez5bt93fBbxGQBrz65V9m1FuVCqEplCiKfQ8n73llJaPDuBNUpk6vP02OpsC1fYB+fCvAfgsW2Fp6U0l63fQOPBZVupx+zRKl0N/hRcaxXdbsvXsOdPmBRRMStmc9n2Hax8Vct6NniKdWTOSNbyZJuz3T7nXojeDc+a/8VvMrnZ8G0HqNsDRn5nvXnKgj5Ti+H3aXgttWwFcy71HNl52YR5Xf8wnZOXw7nUczT0Mi+XeXZeNlm5WXg4lG4nJUOfQXxGPEGuQdwy6xYG1B3AO13fYeWZlXQJ6oK7vZXNPIpqi8VKZAohfIGPAW9goDUUtTVYFLmIrNwsxjcpojpWi9u0JBEuxShFc+j6ZNnHCG4H4/4suZ2dk1aIIjWm+G3f7HTNvm3OStwadHkC0i9qDyK1mmpK21JIqe1GlKaoSEgnbfs89YIWc28OkWvg3Pbrj7W5UwuHOr3xWsWvpFNamcsWX5gvX3lh5wR+jQH45+g/LItaxt+D/64w+/Vfh/+ihW8L2vnfbA7KtzO/ueVNnmn/TImr5HwWn1rMp7s+Zd7weYS4me+J72znTF2PugAsGLGAPJnHmZQzPL/heV4If4HxTcYTkx5DfY/6Zo+tUBRFkd/cQggdcC+andoN6AQEAz9JKReXi3QWYs3ZNVzKvlS0shbC/C/okoj4TSvA0f3Z0vU/vxNqNSs5s5Vx25LYA8Ur6y1faaval89WfOGKfu9oNn3PUO3zuR2QdkGrlV2WreH4I/DXSO0hJ786mKmEdNR+nt8JzUea3i/plFYi1ZB3/S5BvZ6asj67DTZ9du14q/Fld2S0NvtnwclVuLcdSbBrMNl52Tjalv+WfVZuFr8e+pURDUYUqqxBWyWvPLOSgfUG0jXItHve1q8tdze7m9quZU+pGuCiOWtKKZk5ZCbBrsGsPbuWKRFTmDV0Fn7OhSZ5VCjMprhllj3QAMhFq5QVglaUI7hAzvAqwTd9vyk+PeHCx7Vwpi5PWG7Sc9vh+HLo+IgWy2sOV5Lg1/6aUuv+TPFt/ZuD0Gk27uKydV08pMXzVrSiBk2pDf/mWqnGPX/Cvn80B6/mo6H17ZryNHc1d2g+ZCSWzvcgoBXYOpmvrK8kaDsEd80v3FzR6yXtVZVIjYFDcxk2ZArDGgzDUEE+pI62jqy8bSXZeUWHVIZ5hfHf+P9KLn1r5HDiYWx1tjzZ1gK7XwUQQtDct/lVmQbXG4y3ozdSykrlVa+ouhTpYCalzJJSvialfEtK+ZyUchCaQ5kPsEkIUY6Bs2Xjy91fsjO2iAxVUmoFHy5b2Ou13USt1vDRUmxCXDB6mhbnXJaPvYumnEqqbX3xEPibWRbT2uR/iQ3/Bu5eqFUvOzALfhsAX7eF+GOmjyWlVoilXk/Nmc1cbOw004O5yVHqdIZHNhXvV1DVyN+tiTvIw6se5u2tb5e7CFJKnl73NJtjNpdoW469EssdS+9gf0LJ9d2/3vs1T617qlQe5KYS5hXG8x2eZ1PMJu5feT8Z+gyrzaWoOZhlwJRSpgLvCyHWoRX56COlvGwVySxEriGXmcdn4mjrSNfgQrbJ0mK1+Fof85xUSiS0G3jXh4jfofUE8/rG7AEEBLUxrX1g6+KVTFaK9jDS/r6i21QkOhstf3iD3lqN5aNL4Mgi8DJuk69+q/BEMyO+h1pNYPuPsH+6lhymtGYH0DzIk09rir+k1ZCUsOETLVSvoktsWpqrppX9tK3VFk9Hz3IX4VL2JeIz4rmiv1JiW39nfwTCJKX4Sc9POJ92HptycJ6TUqI36MnJy8HZzszdNYXiBkrlbSSl3CqE+AT4HrjDsiJZFludLdvv2F70Vl7iSe1ncZWZSoMQ2hb4ipcgcq15dsqY3ZqTj4Obae0HfQz2xdi2r6YZrWQr68JwcIM2d2gv0JTipTOFJw/RGf987ZzAxU9TtuaGbBWkoxlxzydWwH8falm/vC3oJFcZcPHVCsXE7uex234h15BLTl4O9jb25SaCu707M4bOwBRrm4udC/8M+YfsvOxi5fx277c092lO7zrlU1imd53e3BJyC+fTzvPb4d94pt0z6EQ51CtXVEtK/ZcjpZwJvGNBWaxKkf8kSUZlbemVNUD4fZoT1ZGFpveRUlPWpmyB5+PsDbbFfJFeSdDswQFVQFnfiBCaw9hd825++RrvWft7tM9j/yh7drSUaEg4UXybvFwtXahPQ2h3T9nmq6wY885Hp0XTbUY3VpxZUW5Tp+Wk0W9OP5acWmKyvfdC+gX6zO7D0qilhZ7Pzstm1dlV7EvYZ0FJS0YndGw4v4GFJxcSe6X0qVEVijLF8Ugpj1tKkAojMRLsnMEt6OpTvMUcQmwd4P6V5qX31GdAaBeo38v0Pnm5MGO8ZvMtbHXYbLiWClVRMn+NAN/GcPv0otvsn64lXhk3rXI47FmD3q+C0BHkGsTosNHUcy+/rf7M3Ey6B3envqfpoU+BLoEMbzCchp6FP3Tb6exYMHyBRSt1mcrdze5mcP3BONs6s/rsavqH9i93GRRVH7Un03US3DEbdDpGfLeFz1eXsKoyF/dAbXUYvdu0NJv2LjD+b80Waio2tlqsdVEFRPJtsMortWRCOmn2/6K2X3MyYP2HULtj9X4ACmgJ/s3RCR0vdXypXEOQsnOzeb/7+zT3Mb3gjBCClzq+hKeDJxevXLzuXEp2CoPnD2ZD9AaTvcYtiRACXydf/jz8Jy9seKFSpHBVVD2UsvaoDfV6kJql50B0Cn9tO0uW3sKeohcPwy99YNfPJbe9fE7LC24uga0L9wg3GGBKmJZVTVEyIR218K/kqMLPR++EjCTo/271fvjJzdEc+44vZ/bx2fSf25+4K9Yvc3o8+TiDFwxmWdQys/um5qQyYtEIph2Zdt3xtJw0Gno2LFUCFEvyQMsH+HnAz4S4hVSqimaKqkHNVta52bD0eTi/i1Px6QCkZOpZdeRiCR3NxL85NOwHG6eUXOBjwWPw53Dz5whsrTliZV6+/vil00abtaf5Y9ZEQjppP4vyrq/fC549opkqqjM2drD3bzj2L50DO/Nyx5dxtLF+YhR/Z3+ea/+cyQlOCuJu787/evyPu5rddfVYdl42Qgi+7fvtTelKyxt7G3s6BHTgz8N/MmbxGM6knKlQeRRVi5qtrJNPa6vdS6eJNCprVwdbZu2ywjZVv3e0EKrNxaSaNOTBhb1akQdzKRAbex3lXcO6quPbWCtzWpiyPr1Je8ArTRx3VUOIq7s1ddzrMKrhKJKykswe5nTKac6nmVboIjsvm51xO7mj6R2lDhfrV6cfx5KPsS9+HwBLo5YyeP5gTlyysHmrDAxrMIzH2jxGqHsol7MuV7Q4iipCzVbWBTzBIxPSsbMR3N+9HlsikzifbOFEBgEttHjr7T/C5SK+vBJPaDHf5niCXx3/WmzsdVw8rGU4q9XU/DFrIjodtB4PnnWuP558GqaNgvUfVIxcFUFgK632dm42L216iWfWP2NW96TMJG5fejvrzq0jQ5/BzGMzSclOKbL9f+f/47kNz7H74u4yif3e9veYflRzEOwa1JVn2z1LmGfFrqoL4uPkw/0t7mf12dX0nt2btefWkqHP4FJWxVY4U1RuariyjtR++jTkVHw6dX1cmNAhBCFgdoQVyt71fk37ufnzws/H5GcuK6aGdVG4+sEjG28ujBF3SAsxsit/x5oqy+BPocdz1x9b954W193psYqRqSIIbA2GXIg/wn3N7+OVTq+YFPecj7ejN8+HP0+XoC5sj93OBzs+4MSlE0SnRbP27Fr0efrr2ver04+f+v9Ep8BOpRbZRmfDzwN+5oMeH7A/YT9RKVHc0/yeSpnyMzwgnGfbP0vnwM4sPb2UPrP7cD71PCnZKeTkVYkaSYpypGYr68RIcPUHR3ci49NpWMuVIE8nbmnkx9zd0eQZLJz+3DNE8/Tu+1bh52N2g71b6etqB7a+uRBGcpTaAi8NKTGQZvRdiNmjpTLtOknz7q8pFCgS06ZWG7wdvTmTesakrjOPzeSbvd8wsuFIGnk1ok+dPswdNpf2/u1ZcmoJz214jpScFI4kHeFgwkGi06L56cBPNPVuWubEIfXc6zHtyDTuWnYXb255k1xZTF2ACsTb0ZuJzSfiYudCuH84T7R9gtputfl+3/cMmjcIfZ6elOwUsx6QFNWXmq2sk06CTxjZuXmcS86gYS0tC9j48BBiU7LYeDLB8nM2GgBOnlpazRuxd9HO60p5W05vhH/GkZORxldrTpKYng2PbYVhX5VJ5BpHdjp82QIiftVCuFa/Cc4+0PWpipasfPGqB8O/hfq9MEgD96649yZP66KISoni+KXj2IhraT0bezdGJ3Q81Ooh/h78N75Ovkw9MJVn1j/Djtgd/Hzg52KLdpiKEILtF7bTP7Q/P/b7ETtd5Y+Fr+dRjwdbPogQgn6h/bi/5f3Y2dgx+b/JPL728YoWT1EJqODixhVMt2dA6DiTmIFBclVZ923qj4+LPbN2nqd3YysUaIjZrdk/x/11ffKTAe+VbdzsNDi5kgN7tvDFmjz2nb/Eb/d2QDgWXwhBcQMOrtpuxPkdWvW0M5tg0Cdlz45W1RAC2t0NaF8UX/b+krrudYvtkmfI41DSIV7t9Cr6PH2h28+2Olta+GrZ9N7r9h5nU8/SwrcFvev0NrkmdUl80/cbHGwcLDJWedMhoAMdAjoAmjNawQceRc2lZq+smwyGxgOveoI38NOUtb2tjtHtgllz9KK2OrU0tZqDg7u2YjMYc5Znp4M+s2zjGrctk07uAsD75DwSvukDWallG7cmEtIJoiOgdge4Y07lLYJibeIOwrr3IS+XZj7N2Bu/t1gP5jkn5nDXsrs4nHQYOxOyu7nZu11V3JZS1ECVVdQ3MrLhSAJcAhi5cKTJXvWK6knNVdaXzsKevyAjmcj4dIS4pqwBxncIIdcgmb8n2vJz2zlCn9c1z+3D87Vj+6bDh8HX7KSlwT0YnH3QxR0gPNSLgZ7ncEg6zvkr6sncbEI6QU46JBzVTBPF5V6vzlw8Ahs/haSTnEk5w4sbX2RHXNEV3kY0HMG7Xd+lmXezchSyeuPl4EWQaxDZuVZYOCiqDDVXWZ/dAoufhMxLRCakE+zphJP9NaXWsJYb7UO9mLXrvHUcPFqOA/+WsPZdLXb3wh6t2lFZ6iILQa5/KwIzT9ClgQ893eM5TigvzjuIwdLOctWdkI7az6XPFd+uulOgXGZTn6bMHDKTvnVuriCnz9Pz6JpHiYiLYFTYqErpfV1VaejVkO/7fU8DzwYVLYqiAqm5yjrxpBaK41nnqif4jYzvEMKphCvsPmuF+EedDvq/DZfPQsRvmh07qF2ZU1hecAyjkThP5zquOCQdxbNuG7ZFJfHXtjMWEbvG4BkKfd6AoV9WtCQVi28Y2DpB7H7sdHbY6GyYdXzWTc2Ss5JJykyqkEIZNYEpu6Zw9/K7K1oMRQVSc5V10knwqkeesCUqIZ2Gfjcr6yEtA3Gxt2HWLivZihr01ZzcajXTEqKUJhnKDSyzH8AdeW/TziMN9FcIa9WZXo39+GjFMaIS0ssuc01BCOj5PPjX8O1cnY2W0MeYbGdzzGY+3fUpqTnX/CAiL2n5CopadSvKTgPPBrSt1VaFcdVgrOINLoTwAGYCNsAVYDzwA9AMWCqlfN8a85pFYiT4hhFzKZPsXEOhK2sXB1uGtwli4d4LvDmsGW6OFg4BEQL6vwNRG7TPwaVIM3oDK2Od0QWH42RMrygCWvBxWHMGfLGR5+bsZ+6jXbHRWW+LMjtXK4LiYKvs5JWJlAw9Hs6l/PsNbA0HZoPBwNhGYxnfeDxu9m4ASCl5fcvr5BhymDdsngUlLoGMZCjM0c3RU6vvrs+EtELqR9s6gnuQFpJ36XThY3sbS3OmXoDcApXydHZa4Z/y3uKXklH1BiNt7Mk15JrkuKeoflgrdOtO4HMp5WohxA/ABMBGStlFCPGbECJMSnnSSnOXjCFPSxYS1o/IBC3euTBlDTAuPIQZO8/z74FYbu9Yp9A2ZeaKMZ47qBSZywqQkZPLwegUpoZthyt14Imd4FUXf1sH3h3RnKdn7mPqxige62U929ej03aTqc9j5sPVvNBFFWLe7miem7OfFsHujG5bm+FtgvB1NcNbusUYbffHkIuHgwcLIxciEIxoOAKAT3p+QnJWsvXt1Hl6Tcna2sOOH2HDxze36fUK9HoZonfBn4WUMK3bA+79V3v/dREPx28bU6LOe1DzbSnIiO+h7Z2lvwZzyUqFn3uT4+rPIJdsxjYey6OtHy2/+RWVBqsoaynl9wU++gF3AV8aP68CugPXKWshxMPAwwB16lhJKeaTmw2dH4P6vYiM0baGi1LWbUI8aezvxsxd562nrBvdCg+s1lYEZWD32UvkGiTtMrfB/q3XpR4d3jqIlYfj+GL1CXo38aNJgOVjhlMy9Gw8mUieQbL7bDLtQy0XiqMoHTGXM3l78WGaBrqjE4J3/z3CB8uO0quRH6Pb1aZv01o42pWwCxLa5boqY0ujlgJQ26023+z9ho97fEybWm2seBVoSnrxk9qD7e2ztFri+SvgguRn6/NtDKN+uvm8S4EiLIWdL0iPydBu4rXPW77SEuWUp7JOj4ekSOyTIhk24Hma+dRws0wNxqpJUYQQXQAv4AwQYzycDNy0hJRSTgWmAoSHh1vXMGPvrG0/A5F79+Prao+nc+GhOUIIxnUI4b1/j3AsLtUqSg4Ht2vex2Vge1QSNjqBS2g72PWjVuKwrVYuUAjBeyNasPP0RibP2s/CJ7phb2tZl4X/TsSTZ5DY2Qimbozip7uVsq5IDAbJS3MPkCclP93Vnjo+zpy4mMb8PTEs3BvD2mN7cHO0ZWirIG5rF0z7UK+iV8cnVmllMxv05oteX+Bi53K1QEf+lrhVWf0m7J+h5de3sYWAltqrKNz8tcI5RSFE8edBK2tbkKC24BZgusxlwZCn5WX3bQiPbIKfevCMXSCptdpgkIYyp2RVVD2sdseFEN7AN8D9QDqQX0nC1ZrzmkRiJFzYB1ISGZ9+XXx1YYxqG4ydjbCeo5mF2BGVTMtgD+z8GmoHTq2/7ryPqwMfjGrJkdhUvl1neSvE6iMX8XW156Ee9Vl15CKnE69YfI7yZOupRFKz9CU3rKT8s+MsmyMTeW1IU+r4OAPQyN+Nlwc1YcvLffj7gU70a+rPwr0xjPlxG72m/MdXa06Spc+7ebB172krSyBP5jF84XCc7JyYOXQmznbO1r2Qrd/A1q+hw0PQ8wXrzlUcfo3B0QNyLFyR70akhOUvwd+3abuAAS3Bqx5rjs2m24xunLp8yrrzKyolVlGaQgh7YA7wipTyLLAbbesboDXaSrvi2PED/DkcaVTWRW2B5+PtYs+A5gEs2Btz1YGqspGZk8f+6Mt0ru8D9XpqB1uNv6ndrc0DGN0umO/+O8X+85ctNn9OroENJxLo06QW93ari51Ox6+boyw2fnnz+5bT3PHzDl6ae6CiRSkVZxKv8OGyY/QI8+WOQsw3NjpB9zBfvhjfhl2v92PK2NYEezrxxZoTfL66kNrPga0h7gBIiZOtE5eyL3Ew4aD1V3j7Z8Gq16HZSBj0cfk7d93I2W3wWRMt1NJabJoCu37Wfue2Dto1Nx5M87hInmz9ePnsZCgqHdb6T3sAbav7NSHEf4AA7hZCfA6MA5ZaaV7TSDwJvg1JuJJDalZuicoatOIelzP0rDpchgxjVmTPuUvo8ySd6ntrK4A3krTMW4Xw1rDm+Lk68Nyc/YWvokrBrjPJpGXl0q+pP7XcHBnVNpg5EdEkWSNdq5VZvP8C7/57BD83B5YfimPPuapVZzjPIHl+zn5sbQSfjGlVouOXq4MtY9rXZvpDnRndLpg/tp4h5vINqW8DW0NGEqTGYG9jz/qx63m41cNWvAq0FebhBdrD5+ipWhhZRROgpUZl67fWGX/3n1p611bjoX+BWgE9nyfwmSPc1+pB8mTlXDAorItVlLWU8gcppZeUspfx9SfQC9gO9JZSFl2BvjxIOgU+Da/mBDdFWXdv6Euwp5N16lxbgB1RSegEhId6aQdsinZH8HCy45MxrYiMT+ezVcctMv+aoxext9XRPcwXgId61iM718C07WctMn55sflkIs/N3keHUG9WPtMTX1cHPlp2rErFt/66OYqIs5d4d0RzAj3Mq2P+3IDGADf/XRTIZAZgZ2NnXe9vg0FbUY6fBhOmayvMyoCDG4TfC0cWaimLLcmxpfDvM9CwP4z47vrqe87eYOfISxte5JHVj1h2XkWVoNxsx1LKS1LK2VLKuPKas1ByrkBqNPiEceqGAh7FodMJxobXZtPJRM4nW9lmVQq2n06mRbCHybHgPRv5cWenOvyy+TS7zyaXaW4pJWuOXqRbAx+c7bWHhIa13OjbpBZ/bTtrsdW7tTkYncIj0yJo4OfKz/eE4+1iz9P9wth5Jpm1R+PLPP683dHM3W2FXPMFOHExjSkrT3Brc39Gtgk2u3+wpxP3da3Lgr0xHLlQoACMf3MQuqvK2qoknIAfukLsAc2pzaGSbft2elT7XWz/vuS25nBonubENu5P7bpvZPsPjD+yjqfaTKpSD48Ky1DzXAqTjXZUX21l7WJvQ6CHo0ldx4aHIATMsfIXrrlk6fPYd85orzaDVwc3xcfFnqkby2ZbPhmfzvnkTPo187/u+EM965N8JYd51iiGYmFOJ17h3t934ulsz5/3d8TDSfuynNAhhPq+Lny04hi5eYZSj38wOoWX5h3g7cWHycjJtZTY16HPMzB59j5cHW35YFTLUq98H+/VEHdHOz5acezaQXsX6P6sRbLsFUvqBfh7NGQkaqVKKyPuQdByLOyZpiVnKSv5lfdGTYW75mu/68Jw8aNjwhma6vO4mFE5zXEK61HzlLU0QIM+UKsZkQnpNKjlavKXWrCnEz3C/JgbcZ68SlQYY++5y+TkGehUz7xQKRcHW0a3q83ao/EkpJXetrzmqPbF0bfJ9cq6Uz1vWtX24JdNpyt1IZH41Cwm/rYDg5T89UBH/N2vPbzZ2eh4cWBjIuPTS70qztLn8dycfTja2ZCencvSA4Vk1rIA362P5FBMKh+OamFe0pMb8HC2Y1Lvhmw8kcCWyMRrJ/q+qeUEsBaZlzQP6MzLcNe8wuOoKwtdJkG9HpBdxvKzl8/BTz0gerdmunLyLLptw75k29gyfNsrzDw2s2zzKqocNU9ZB7aGuxeAn/YFXFhO8OKY0CGECylZbDqZYCUBzWfH6SSEgPC65sc1jwsveynQtUfjaRnsQcANOxRCCB7qUZ/TiVeuKvTKRmqWnnt+30VSeg6/39exUJPIrc0DaFfHky/WnCAzx/wt/S/WnODExXS+vaMt9f1crBICeDA6hW/XRTKyTRADWwSWeby7u4QS7OnE/5YfvfaglZUCJ1ZqPy2NPhNm3A5JkTDhn2s28spKQAu4YxZ41S39GFeSYNpoSDkPdib4Fjh54RjShY8z7RkVNqr08yqqJDVPWafHQ56e1Cw9F1OzaWCCc1lB+jX1x9vFnt+3nKk0dqPtUUk0D3K/unVrDg1ruRIe6sWsiNKVAk1Mz2bPuUv0bVp4ac9BLQII9nTi502VL4wrS5/Hw39FcPJiGj/c1Z42IZ6FthNC8MrgplxMzea3LUXkky6CiDPJTN0Yxe0d69CrcS0mdAgh4uwlIuPTLHAFGvkrdx9Xe94Z3sIiYzra2fDcgEYcikllyYEL2sHYAzB9HJzfZZE5ruP4Mi1F6KifoP4tlh/fGkgJJ1fDyTXm981Oh+ljNUV9+0zTC8Y0HkTXi5Gcv7ALfV7VzQGgMJ+ap6ynj4fp4646l5niCV4Qe1sdj93SgA0nEliwN6bkDlYmOzePvecu06meefbqgozvEEJUwhUiSlEKdP2xeKTUHmIKw9ZGxwPd67HrzKVKFQKVZ5A8O2sf26OSmTK2Nbc08iu2fYe63vRv5s8P/50yORwtIyeX5+bsJ9jTideGNAVgdLva2OoEsyMsZ8fPX7l/dFur0hfrKISRbYJpGujOpyuPa/kF8jOGxVnByazFbfDMQWgx2vJjW5M1b8Oq1zTFbSp5epg9ES7shTG/QWhX0/s2HsRWV3ce2/EOx5KPldxeUW2oWcpaSm2bzbuBWWFbN3J/93qEh3rx1uLDxKZkltzBiuw/n0J2rvn26oIMaRWIq4NtqbZn1xy9SIC7I82Dik7DOq5DCO6OtvxSSVbXUkreXHSI5YfieH1IU0a2Nc1r+qWBjcnIyeWbdZEmtf9o+THOJmUwZWxrXB00L3lfVwf6NfVn3u5ocnJL77CWz+6z+Sv3EHo3Lnx3o7TodIJXBjUh+lImf28/p9lTvepZ1iM8LQ52/aI5WbkHWW7c8kAI6PokJByDSDNW12e3QtR6rVZ6kyHmzeldn06PRvDrgF8J8wozr6+iSlOzlHV6vOYQ4hvGqYQr2NkIQr3NT5VooxNMGdua3DzJi3MPVOh2+I4ozV7dsQzK2tnelmGtg1h6INas9JpZ+jw2nUykb9NaxTrpuTrYcmfnUFYciuNsUsWnIP1q7Un+2XGOR26pz4M9THdialjLjfEdQvhnx1nOJRUfvrf5ZCJ/bTvL/d3q3eSlP75DCElXclh3rGx2/IycXJ6bnb9yt06Bh56N/Oje0Jdv1p0kJVMPga207XBLICUsfgpWvqZtB1uQ3DwD8alZJTcsK81Hg1uQlg7VVOrfAk/sgvb3lGpKD5daeNi6sDNma6n6K6omNUtZJxlXRD7ayrqujwu2NqX7FdT1deHVIU3ZdDKR6TvPWVBI89h+OokmAe5FFiIxlfEdQsjU57Fk/wXT545KIiMn76aQrcK4t2tdbHSC3zabZ/O1JJk5ebw87wBfrjnJbe1q8/LAJmaP8Uy/RtjqdHxaTDKZ1Cw9L87dT30/F14c2Pim8z0b+RHg7sjMMjqafbsukjNJGXw65trK3Rq8PKgJlzP0/LjhlOb4dem0ZZzM9k6Dkyuh39vgFVr28YxEX8pg3E/b6P7xeuvnRLC1h86PwumNWr2B4tjyNWz6THtI8W1Y+jkTI/l17mje2fJ6pfGbUVifGqasjcUrfMI4lVByTvCSuKtTHXqE+fLB0qMlrrSsQU6ugd1nL5VpCzyf1rU9aBLgxmwzFMiaoxdxtrehiwnx3f7ujoxoE8zsiGguXckpi6il4nhcGsO/3cysiPM83qsBH99Wujhkf3dHHuxRjyX7L3Ag+nKhbd5bcoS41Cw+H9em0PKTNsYEOxtPJHDhxrSeJhKbksmvm08zsk0QXRqU3l/BFFoEezCyTRC/bT5Nom8HaH172YtZXDoLK17R6kt3tFxGrhWHYhn81SZOXExHbzCUT8bB9veCvRtE/FZ0m30zYPUbEHfIPPt2YXjXY1JGLv/YNbB+DXFFpaFmKevcHHAPJts1iLNJV8qsrIUQfHxbK2yE4Pk5+8s9lvhgzGWy9Aazk6EUhhCCceEh7I9O4WhsybGjUkrWHY2ne0PfkushG3moR30y9Xn8s6P8UpBKKZm+4xzDv93MpQw9f93fkRcHNin1jgrAwz3r4+1iz/8KSUO65shF5uyO5vFeDYv0LgctZM4gKXXs9uerTiDltfSg1ua5AY2REj457AGjfgT3MoSHGQyw6Ant/Y1pNUtJlj6PNxYe4tG/91DP14VlT/WgZ5gfcyKirZ8TwdFDCwcd9HHh50+s0q633i3a766s16uzIaBePyJitnI4vhwyyikqBTVLWXd6GCYf4UxSFgZZOueyGwnydOKt4c3ZeSbZ7LCefM4kXjFr+zmf7VFa9qSy2KsLMqptMPY2OpMczY7EpnIhJcukLfB8Gge4cUsjP/7YWj4pSFMy9UyavpdXFxykYz1vlj/dgx5hxXt9m4Kbox1P9WnItqgk/jtxLd4++UoOL88/SJMAN57qW7zzT4i3M90a+jA74rzZD3nH49KYtyeaiV1CCSmFz0VpCPF25u4uoczdHU3k6TNw8XDpB8u6DPoMuPVDi2x/R8anM/K7LUzbfpaHe9ZnzqNdqePjzPgOIcSlZrHxRDnkRAjpoMVK594QKXB+l+b5HdACxv9tsRznusa38q6HI/8eKGY1r6hW1CxlbVwFRZqRE9wUbmsXTL+m/nyy8rjZ8bPz90Qz+OtNPDljL4vNVNjbo5Jo7O+Gt0vZ7NX5eLnYM6C5Pwv2xpSoTNcciUcI6NPEPA/kh3vWJzE9m0X7rBv2tvfcJYZ8vYkVh+N4aWAT/ryvI35ulisGcUenUEJ9nPl4+bGrK7c3Fh0iJTOHz8e1wd625H+t8R3qEH0pk62nksya++MVx3BxsOWJ3mWwe5aCSb0b4uJgS/rsR2Du/aUfyNkb7l8F7SaWSR4pJXMizjPsm83Ep2Xz+30deHVw06u/+/ycCOVWh37vP/BFcy0DG2g7CP8+C24BcOdccCw6YsJcbBv2Z15cEs/nFpGaVFHtqDnKOk8PH4XC9h+JjE9HCMspayEEH45ugYu9DZNn7zcph/SV7Fwmz97H5Nn7aRHsQevaHryx8JDJHqz6PKO9ur5lVtX5TOhQh5RMPauOFO+pvPbYRdqGeJqd1rJrAx+aBbrzs5VSkBoMkp82nGLsj9uQEmY/0oXHejVAp7Osbc/eVscLtzbmWFwaC/bGsGT/BZYeiOWZfo1oVkwYW0EGNPPHw8mOWWbYVbedSmLdsXge79UQLws9pJmKl4s9j/dqyIa0IGTiCfPt1oY8WDQJ4g5qqTXLYG9Nz87l2Vn7eGHuAdqEeLL86R43ha7Z2+oY3TaYNUcvlimdrskEtIArCbDnT+2zTgd3zNS2yF0tG1aHgxt5od34MfUwGfrKV1hIYXlqjrK+dAayU8DRnciEdII9nXCyt1x93Fpujrw/siUHolP44b9TxbY9FJPC0G82s3BvDM/0C2PGQ535fHwbsvR5vDz/oEkengdjUsjIybOIvbogXRv4UNvLiVm7ivZwv5iaxYHoFPoWkQilOIQQPNyzPpHx6fx3ouyVrAqSmJ7NfX/s4n/Lj9G/mT/Lnu5B+/ySoVZgSMtAWtf2YMrK47yx6BCtQzx5pKfpoWCOdjaMahvMykNxJjndSSn5aPlRAj0cua9b3TJIXnru61aXGMdGCGlAXjxkXuetX2se4PFlS+ZxMDqFoV9vYvH+CzzXvxF/P9jpunzuBRnfQUunu2BvORSTCWyt1d7e/oMWkpaVAh61wbueVaY71+Nppmad4dTl4r9vFNUD68V7VDYSr3mCR8aX3RO8MIa0CmTl4SC+WnuSPk1r0TzI47rzUkr+3HqGD5cdw8vFjukPdb6qbBv4ufLSwCa8++8R5kREM65DSLFz7bCwvTofnU5zNPt89QnOJ2cUahPNLxdZVNaykhjSKpCPVxzj5XkHqetjuUQppxLSScvO5f2RLbizUx2re8oKIXh5UFNu/3k7DrY6Phvb2mzHtfEdQvhj6xkW7ovhvm7Ff6kvPRjL/ugUPh3TymSnPkvjaGdDz1v6wtqP2DLtXea430Osbe1i+9zRqQ4jgy7D+g+h6XBoOabU8688HMek6XvwdXVg5sNdSvz7D/N3o32oFzN3neehHvWt7z3d9Sn4ZwzsnwFt7oA6na02VeegzmwbtwnnnHSrzaGoPNSclbUxxjrPuyFRCeYX8DCVd0c0x8vFnudm79dSNBq5dCWHh6ft5u0lR+ge5svyp3vetCq+t2tdOtf35t1/jxB9qfitrR2nk2hYy7VM1ZWKYkz72ghBkWEva49eJMTbiUb+pfsd2tnoeHNoMxr4uWKjExZ7ta3jxaInunFX59ByC2np0sCHyf0b8cX4NqV6AGwa6E7r2h7M2lV8bvacXAOfrjxOkwA3RrcrXjlam0Fdw9ntM4wuOVton7MLG53AS6bgKVNuuifnkjP4bPlB5IJHwcEdhn5R6u1vKSWfrzpx1dvb1AfV8eFaOt3dpUinazYN+2kVucb/bVVFDWBvY89/M4bx67yxVp1HUTmoOSvrpJPg7EtMlgPZuQarrKwBPJ3t+fi2ltz/RwRfrTnJiwObsPN0Mk/P3EtiejZvDG3G/d3qFqpMdDrBp2NaM/DLjbw49wB/P9CpUFtrbp6BXaeTGdXOtDSZ5hLk6cQtjbSwl2f6NcKmgAyZOXlsjkzk9o5lW7kOahnIoJZlrw5VGSjJ87skxnUI4bUFhzgQnULrIsK9Zuw8x9mkDH6/r8N196MisLHR0f7JvyE9gYm29kx09IDVb8G2byFsALSeAI0Ggq0Dyw7GcnLWq4icAzD+H3DxLfW8+6NTOH4xjQ9GtTDLXj+kVSDvLDnMrF3nS1WZziyEgFs/sO4cBdjh5smR5GM8kJGsOe4pqi01Z2V9+Rz4NCQyQfPWtpayBujTxJ/x4SH8uOEUry04yISp27C31THvsa480L1esUouxNuZN4Y2Y+upJKZtLzwe+fCFVK7k5JWpeEdJjA8vPOxlc2Qi2bkG+psRsqUonuGtg3Cysykyo1lalp6v1p6kS30fepVQcKRccfXTYoxB2/Lt/DjE7NFClaY0gn+fpV9gNol2QfznMQKaDi3TdLN2ncfRTsew1ublEHdx0NLp/nsgljQz0uneiKWzhUkpyzzma51eYc6FODi5ykJSKSorNUdZ37UA7phVpgIe5vD60KYEejjxz45zDG0VxL9PdqdVbU+T+o7vEEKvxn78b/lRTifenEt7x2kt1MfSnuAF6dvUH59Cwl7WHr2Im4MtHay9QqlBuDnaMbhlIEv2XyAjJ/em81M3RpF8JYdXBjepvBmr/BrDgPdg8hG4a762wt4/E3udxKH9nTyYMIFEE6uVFUZGTi5L9l9gSMsg3B3NryyWn0733wOxpZr/xMU0On24lj+3nilV/xu5nJHD8G+38EIZawvk1mrB+wHBrD8ywyJyKSovNUdZ63Tg5ElkfDq+rvZlzqVdEm6Odvx5f0d+mRjOVxPa4GbGF0x+ZjQHWxuem73vpgxMO6KSqe/nQi23wj1gLYG9rY7R7a4PezEYJGuOxtOzsZ9JccQK05nQMYT07FyW3qBM4lOz+GXTaYa1DjL5Ya9C0dlAw75w28/wwinwrnfNI3tP6WPrlx6IJT07l/ElOF4WRZsQTxr5u5YqH7s+z8Dk2fuIT8vmrcWHzc6HcCOZOXnc/8cuDsakMHd3NEtK+QAB4GTvwmYXF84mHNIyNCqqLTXjGzf2APx6K8TuJzI+3WLx1SXRsJYr/Zr5lzoH9bsjmrPn3GWmbrzmMZ1nkOw8nWzVLfB8bgx7ORCTQmJ6Nv1L6QWuKJrwUC/q+7nc5NT3xZqT5BoMvFBOaUUtir0WSRDm70a7Op7Miijeia44Zkecp76vCx3qli4UTwjB+A512H/+MsfiSk6nW5Dv1kdyKCaVrya0oWM9b56bvY9NJ0uXFU2fZ+CJ6XvYe/4y397RlrZ1PHlj4SEulrJCmBCC5Z0+5F6v1pCRWKoxFFWDmqGs44/C+e1IGwerhW1Zg+GtgxjUIoAvVp+4+gVzNDaVtOxcOltxCzyfhrWuhb1IKVl79CI2OkGvxpXIblpNyM/NvuvMpaummsj4NGbtOsednUKp41M+aUWtxfgOIUTGp7PnnPke2ZHx6ew6c4lxHULKZAYY1TYYOxthVkazg9EpfLsukpFtghjRJpifJ4bTwM+VR6ftLrKQS1FIKXll/kHWHYvnvREtGNoqiM/GtiY7V6sGV9oHmd3uXjzk70OKg8pmVp2pGco66SQIHQn2QaRm5VYZZS2E4P2RLXBztOW52fvJyTWwPcpory6HlTVoX7L5YS+rj1ykfaiX1U0INZXR7YKx1QnmGFfXH684jrO9LU/2Kd+0otZgaKsgXOxtSpX6c07EeWx1gtFljH7wdrFnQPMAFuyNuS6ssiiy9HlMnr0PH1d73hneAgAPJ8285eViz32/7yrUp6QoPll5nLm7o3m6bxh3ddZyotc35ldYfzyhTBXCUrIvkxC1vuwVvRSVlpqhrBNPgmcokUmaTaeqKGsAH1cHPhzdksMXUvl2fSTbo5Kp6+NMgIf17NUFGdIyEBd7G75Yc4JjcWlqC9yK1HJzpE+TWszbE822U0msPnKRx3o1wMcKsfTljYuDreZoabQ9m4o+z8C8PdH0aVLLIj4a48NDuJyhZ9Xh4tPpAnyx+gQn49P56LZWeDhf8znxd3fkr/s7IoGJv+0wKUXwr5tP88N/p7ijUx2e6Xd9qN89XYz5FZYcKVX97fCAcGYHD6fhrHsg/ojZ/RVVg5qhrJNOgW8Yp8rJE9zS3No8gNFtg/lufSRbIhPLbVUN2pfs8DZBbInUVvR9m1o4x7HiOiZ0DCExPYfH/tmNv7sD95eQ1awqMb5jCBk5efxrhoPW2qPxJKbnMKFj6RzLbqR7Q1+CPZ1KXMVGnElm6qYobu9Y56ac46CtiP+4rwNJ6Tnc8/suUosJCVu0L4b3/j3CwOYBvDeixU1b+fn5FQBenHugVDnzv8mJ4SU/Hzi+3Oy+iqpB9VfWBoOWvcyYZtTVwZaAIvIIV2beGtYcP1cHMvV5dG5QvmFT48K1L8r6fi7ULyfnvJpKzzA//N0duJyh59l+jSyav76iaRviSVgt8zyyZ+06h7+7Az0tUNoUNMU4Nrw2myMTi1zFZuTk8tyc/QR7OvHakKZFjtWqtic/3d2eyPg0Hv4rotBKdRtPJPD8nP10qufNlxPaFJnQJj+/wraoJP7adsbs67Jz8sTeybtEZa3P07Pu3DoyczPNnkNRsVR/ZS0EPLoJOj9KZEI6DfxcKm+sajF4ONvx2bjWNPJ3tUhNZnNoE+JJ/2b+TOxc9trDiuKxtdHxeK+G9AjzZUz7ik0ramk0j+wQ9p2/zPG4kkvJxqZksuFEAmPbh5idc704xhofPufsLry4x0fLj3E2KYMpY1vj6lB8ksceYX5MGdua7VHJPDvr+jDL/ecv8+jfu2lYy42f7wkvMZ97fn6Fj1YcIyrBvHzfj7Z+lPcajIOY3ZB+fYEcKSWHEw8TdyWOvfF7eXr902yI3kBSZhIGWXKFQEXloGYoa98w8KzDqfgrNKhiW+AF6dbQl1XP3mKVfODFIYTg54nh3FuNtmQrM/d0rcu0BzpZVEFVFka3q22yR/bciGgM8trOjqUI9nSiR5gfcyPO35TDYPPJRP7adpb7u9UzuaLdiDbBvDm0GcsPxfHmokNIKYlKSOe+P3bh7WLPn/d1MCmRy3X5FeaYVmo3n/ScdMYlbWKWmwucWAlA3JU4olKiSMpK4o5ldzDnxBzCA8L5sd+PtK/VnvH/juebvd+YPIeiYrHat4EQwl8IsanA51+FENuEEK9ba87iSMvSE5eaVeXs1QpFdcLbxZ4BzQJYsDe6WI9sg0Eye/d5ujbwsUrY2oQOIVxIybouXjo1S8+Lc/dT38+FFweaF9d+f/d6PNarAf/sOMd7/x5l4m87EcC0BzpRywyzW35+hb3nLjN1k+kV6VztXQn1aYJjUHuOo8cgDdy59E6+3P0lvk6+fNPnG+5pfg86oaNbcDd8nXwZHTaaW+veqlbXVQSrKGshhBfwJ+Bi/DwasJFSdgHqCyHKVvmgFJxK0EIsrFVtS6FQmMa4DiFcytCz+kjRHtnbopI4n5xZ6oxlJdGvqT/eLvbXOZq9t+QIcalZfDa2dalKkL54a2PGtq/Nb1tOk3wlh9/v60A9X/Njn4e3DmJwy+vzK5jCp7d8ygq/YCafmYdA8F6393gh/AUAetbuibu9+9W2Qggeb/M4ddzq8MjqR1gYudBsOasMhjyIO1jRUpQZa62s84DxQP5fWi9gtvH9KqD7jR2EEA8LISKEEBEJCaXLDlQc+YkmqvI2uEJRHcj3yC5uK3zWrvN4ONlxa/MAq8hgb6tjdNtgVh+5SFJ6NmuOXGTO7mge69WAtnVKnyXtf6Nb8lTfMP64r2Op08MKIXhvRAs8nOyYPEvLr2Aqj7R4iPfD7oCE43QN7kqIe/EPO3Y6O2x1tuhE9TO5AJB6Af4crmWwTIur0nHoVrlDUspUKWVKgUMuQH5i4GTgpmBdKeVUKWW4lDLcz8/yDlSR8enY2QhCvat2JiiFoqpjoxOMaa95ZBdWt/1yRg4rDscxsk1QqVa4pjK+Qwj6PMmvm0/z8vyDNAlwK3O5U1sbHZP7NzK51nZR+Lg68MGolhyJTeXbdSdN7tfGtzltl7yI2PGjSe3tbOz4vu/3DKs/jG/3fsu++H2llLgScmIl/NgdLuyBIVPAkAu/D4LzOytaslJRXo9T6YCT8b1rOc57lcj4dOr6uFRLpx2FoqoxNlzzdJ8TcbNH9sK9MeTkGhjfoY5VZcjPWf79f6dIyczh83FtcLCtPKFytzYPYHS7YL777xT7z182rZOtAzToAydWmLyKFEKQrk9n+enlrDu3rlSyZujNT+ZiNXJzYOVrMH0cuAXBIxu1Eq4O7pASAwsehZxKJK+JlJfm2s21re/WwJlymvcqpxKqTk5whaK6U9vLme4NfZlzg0e2lJKZu87TMtiDZkHuxYxgGSYYHwie6deoXOYzl/z8Cm8uOmR6p8aDIC0WYveZ3MXN3o1/Bv/Ds+2fZWP0RuKuxJXY54r+ClEpUejz9PSb24+/j/xtuozWJDkKdv0CHR+GB9do0UAAju4w8jtIPgVr36lYGUtBeSnrhcDdQojPgXHA0nKaF4Ds3DzOJl1RylqhqERM6FCHCylZbI68Vi3qYEwKx+LSrOZYdiOj2wXz6z3hPHpLg3KZz1w8nOx4oHs99kenmJ6HPGwAIOD4CrPm8nT05Ir+Cq9ufpWv93xdaJs8Qx5RKZqX+qOrH+XVTa9iZ2NHt6BuNPFuYtZ8FufEKsjTQ60mMGkXDP4U7G7wxK/XEzo9Cjt+hNMbK0bOUmJVZS2l7GX8mYrmZLYd6H2DPdvqnEnMwCCrXppRhaI6069ZLbyc7Zi169zVYzN3ncfRTsfwNkHlIoOtjY6+Tf2LzCxWGRjSKhDA9DStLr4Q0glOmJ961NXelZ/6/8TrnV8nOi36aqaz82maM+BHOz/irmV3kZ2XzRNtn+Clji8Bmid6mFcY3+37Dr2h6NSrViHnCiyaBNPHwu4/tGOexZhQ+r4F3g1g4ROQXXJynspC8el5LIiU8hLXPMLLlaue4CpsS6GoNDjY2jC6XW3+2naGpPRsnO1tWbLvAoNbBpqURKSmEOTpRPtQL/49EMuTpjrAdXoYUmM1u7WZGRub+zQnQ5/B3cvvpqtrXdo3GMJb299h6ailjAobRYeADuiEjs6Bna/rF3Exgl8O/ELHgI50COhQ/CQxewovOhLYBgJaaLblqPU3n3cLhIZ9IS8XDszUwrK2fQeJJ6DH89D+vpIv0N4ZRv2oZXuzuz60LteQi62u3NSiWVROqSxMZHw6QihlrVBUNsZ3COHXzadZsDcGL2d70rJzGW/hjGXVgaGtAnlnyRFOXkwjzN+t5A4tbivTfM52zkz2akuLnX/iUncEL3d8GQ8HD+q416GZT7NC+/St05fFoxZT27U2KdkpeDh4XN8g+bS2Te3XCI4sgi1fFjLIW5qyjj8Ki564+XyDvpqyNuReO+9SC+5eAA16m36BIR21F2grc3sXoi5H8dT6p3i/2/t4OXrhYueCr5Ov6WNamZqhrBPSCfZ0qlZFERSK6kAjfzfa1vFk1q7zeDnbU8/XpcxhT9WRwS0DefffIyw5EMvk/iYoa4C4Q1oykDa3mz+hwcCw45vAwQeajuBOE1fnIW4hvLvtXfbG72XW0FnY29hfO7nyNTi7BZ47Bt2fgQ4P3DyAo1HB1+0GzxSSyMTWaIO2dbh23sUP7JxubmsKO3/GsPkL4ifOx889GH9nf3RCx0sbXyLXkMucYXMqTS2JmqGs45UnuEJRWRkfHsLL87Uv3pcGNqk0X46VCX93RzrV8+bfAxd4tl+Yab+jff/Arl+h2XCwNzOT2slV2tbybb9qq+HY/RBSwta2kd4hvQl1D8VGFFgcndsOx5dCnzc0xWrnBE7FJJ+xcyre7ixE8edNJaQjH9hnsXHZnSyYsIFfb/0VgPe6vUdKdgqZuZnMOTGHO5veWeHb49U+6DjPoCXVV2lGFYrKydDWQTjb22CjE9zWPriixam0DG0VRFTCFY7GmugU1Wgg5GXDqUJsvyWx9RvwCIFmI2DV6/DXcC0DmAn0qN2Du5vdzewTs7UkK1LCqjc0e3Pnx82XxUrsitvFZa9QRjcay4MJcbicWHX1XJhXGOEB4aw5t4bPIj7jUKIZoXNWotor68sZOYT6ONMksPLFUCoUCnB1sOXJPmE83LM+tdyqXq358mJQiwBsdIJ/D5joFR7aFRw8SqxxfRMX9sLZzdD5MbCxg86Paqvr/z4yeYis3Cz+PPwn/0b9C8f+heid0OsVzbmrEpCYmchjax7jh/0/0Lzv+4x3C0Msfe6m8qLDGwxn7vC5tKnVho93fsyas2sqSGIQshLmSg0PD5cREREVLYZCoVBUKu7+dQdnkzLY8EIv07bC596vxRM/dwJ0Jq7N8vSaA1jYAC2RCMCyF7VEI0/suJZkpATiM+Lxc/QlaWoPfHP18NhWsKnYreTY9FjmnJjDk22fZFvsNtr4tcHZzhnij8FPPaHFaM1T/AYy9Bncv/J+etTuwRNtnkBv0GOns3zEghBit5QyvLBz1X5lrVAoFNWFoa0COZecwcEYE1NVNB4MVxK0MCVTkFJbTbccc01RA9zyItg5w5q3TZa1lnMttsft4FaXTHb0errCFTXAxuiN/HP0H06nnqZrUFdNUYOWSGXsH9D/3UL7Ods5M23QNB5p9QiLTy1mwr8TSMxMLLSttVDKWqFQKKoItzYPwFYn+PdArGkdGvbTFJBHbdPar3pdSxZy446riy90e1rb0o7db9pY+kzaeDZmXOPxNGo42LQ+ViDXkMv/dvyPPw79wbjG41g8cjH1Perf3LDJYBafymXop//y+YJNHL6QQsGdZzsbrUKZt6M3oe6heDmUrjpbaVHKWqFQKKoIns729AjzZemBWEwyYTp5akrWPbDkthnJEPE7yLzCE6l0eRwmzICAVqYJu+VrnH7szkstH+Zc2jl+2PeDaf0sSHpOOjbChoTMBJKzkxFC4O9yU9FHADacSOCFWbv5LuNFOu59hSFfb2LQV5uYuvEUF1OzrrbrHtydz3t9jo2ufEOBlbJWKBSKKsTQVkHEXM5kz7nLpnVIi4P/PobLRdcPB2D376C/Al0mFX7e3gWaDNYUeUlpOtPjYctXENwenLxYf249CyMXkpJdfpmmd8XtYsC8AexP2M+nPT9lcvvJRbbdd/4yj/29mwb+nvgPeIbuuoPMansERzsbPlx2jC7/W8vE33ayaF8MmTl55XYNBVHKWqFQKKoQ/Zv7Y2+jM90rPOcK/PchHCumflJuNuz4SSuvGdCi+PFWvQE/99FSfhbFho8hN0vLSAY83uZx5g2fB8CRJC3N6OmU05xLPVfs+9KQoc/g1OVTNPZuzC21byHQJbDYVfCphHTu/2MXvq4O/HF/Bxw7PwT1e9Mp8gsW3h7I2udu4fFeDTkVn87TM/fR4YM1vDh3P9ujkjAYys9BWylrhUKhqEK4O9pxS2M/lh2MNU1Z+DQA30bFF/Y4OAfSL0LXJ0ser05nLWHK3r8KP590Siuo0f5e8G0IgL2NPfY29ty+9Ha+3P0lAK9tfo0Pd35Y7Ps/D//J9tjtJctUgOc2PMcTa5/AycaJ//X4X5Hb3gAXU7OY+OtOdAL+ur+jFjooBIz4FnR2sPBxGvg48fytjdn0Ym9mPNSZQS0CWHoglglTt7PqyEWzZCsLFe+ep1AoFAqzGNoqkNVHLrLrTDKd6vuU3KHxINj2PWSlXEvpWZD0ixAcDvVNyK/deDCEdNbirluNvzk72tp3wMYBer183WF7G3u+7fMtqTmpADzb/tmr4U+Fvdfn6fnr8F8MrDeQTgGd+PHAj9waeiv1PW92DpNSMvfkXPrX6c+ktpPI0GdgZ1N8aFVKpp57ftvJ5YwcZj3Shbq+Ba7DozYM+hgWPgqnN0CDPuh0gi4NfOjSwId3RjRn1eGL9GrsV/Lvy0KoOGuFQqGoYlzJzqX9+6sZ2z6E90aWsG0NcHYb/D4QxvyuxRIXhsFgeiz2uR3w2wDo/ZoW1lWQyDWQegHaTTRtrGLQ5+nJzMvkUtYlRi0axRud36BfaD/mnpjLiIYj8HbU8sifSTnDqMWjeKLNEzzY8sESx83S5zHx153sPX+JP+7rSLeGhRTskFLzfA9qU+brMBUVZ61QKBTVCBcHW/o0qcXyQ7Hk5hlK7hDSEZy84cSKm88dXw76LNMVNUCdTtB0mOZElp5w/bmG/SyiqEELl3K3dyfUPZT149YzsN5Adsbu5PPdn3Mh/QJLju7kniXP4ucYzIwhM3igRSHFQW4gN8/AUzP2sutsMl+Mb1O4ogZtOzyojaa0jy7RksVUIEpZKxQKRRVkaKsgEtNz2HE6ueTGOhsY+T10v8Ej+sI+mDEBIn4zX4C+b0GHB8HWWFnr2DL4e8zNyttCeDh44GTrRN/QviwZuZR1++x5ftl0dl04SMePFvDL2my2leD0JaXkjUWHWHXkIm8NbcbQVkElT3xuG8y6CzZ/YcGrMR9ls1YoFIoqSO/GtXC2t+HfAxeKXh0WpPGgm49t+xbs3aDtneYL4BsG/d/R3uflatnNZJ4W221FLqZm8cqsC2yLSmJEm3sY2z6ExftjWHYwjrm7own2dGJU22BGtQumwQ0FnD5ffYIZO88zqXdD7u1Wz7QJQ7tCizGah3ujWyGwtRWuqmSUzVqhUCiqKE/N2MvGkwnseq0fdjYmbJRu+UpLG9rxIS3u+qvWWsGOWz8ovRA7f9ZW5vFHYNw0rSSnlVh/PJ7nZ+8nIyePd0c0Z0z72ldzpGfm5LHqSBzz98Sw6WQCBgltQjy5rV0wQ1sFsXj/Bd5afJjx4SF8dFtL80qxZiTD913A2Rse/k+rp20FirNZq5W1QqFQVFGGtdaU0JbIRHo1rlVyh6j/uBJ/micOt2Zc0o/cKiWTT3cm6ZcdhTa3t9XRu0kthrUKxNPZvvAxryRqirp2R82ObQVycg1MWXWcqRujaBLgxrd3tKNhretXzU72NoxoE8yINsHEp2axaN8F5u2J5o1Fh3n33yPkGiT9mvrzwagW5tdMd/aG4d/A9LHw3/+g39uWuzgTUcpaoVAoqig9G/ni5mjLvwdiS1TW2bl5rMtpw6C0ddjm7KVXzjK2OPYkWvqAvvCsXBcuZ/LGwkO8t+QIfZrUYnS7YHo1roW9bYFVfNcnITUGOj1aeJrSMnIuKYMnZ+xhf3QKd3cO5bUhTXG0Kz7VZy13Rx7qWZ+HetbnyIVU5u2J5nKGng9GtcDWlB2Iwmg0QHOci1wDvV69ZqsvJ9Q2uEKhUFRhnpu9n1VH4oh4vR8OtoUrsTOJV3hyxl6SYyLZ4vg0+j5vYxfQDDxDtYpTRSCl5EhsKvP3xLBoXwyJ6Tl4OdsxvHUQt7WvTctgD/NXqWawZP8FXp1/ECHgkzGtGNjChBzn1iTnCuhsK2QbXClrhUKhqMKsPx7Pfb/v4peJ4fRrdnO2rkX7Ynh1/kFsbXR8OqYVAzbepiVGuW+ZWfPo8wxsPpnIvD3RrDpykZxcAw1ruTK6XTAj2wQT5OlkqUsiMyePd5YcZuau87Sr48nXt7eltpezxcYvMwkn4NQ66PyoRYdVNmuFQqGopnRv6Iunsx3/HrhwnbLOyMnl7cWHmR0RTXioF1/d3pZgTyeIbAN7/9acppy9TZ7HzkazX/duUouUTD3LDsYyf080n6w4zqcrj9O1gQ+j29ZmYIsAXBxKp1oi49OYtyeGBXtiuJiWxeO9GvBs/0amOc+VJ7v/gLgD0OEBrf53OaBW1gqFQlHFeXneAZbsv8DuN/rjaGfD0dhUJk3fQ1TiFSb1bsjTfcOu2WozkrXkKK1vt4iN+WzSFebviWHB3hjOJWfgZGfDoBYBjG5Xmy4NfLDRFT9HUno2S/ZfYP7eGA5Ep2CjE9zSyI8He9SjawMTQtIqgtxsLXe4OYlkTEBtgysUCkU1ZvPJRO76dQc/3tWOhPQc3vv3CB5Odnw1vg1dTYnBtgBSSnafvcS8PTH8e+ACaVm5BHo4MrJtMKPbBhPm73a1bXZuHuuPxTNvTwzrj8WTa5A0C3RndDvNm9vPzTo24cqOUtYKhUJRjcnNM9Dpw7Xk5BpIy87llkZ+fDauNb6uFaP0svR5rDl6kfl7YthwIoE8g6RVbQ+Gtw7iTNIVluyPJSVTj5+bg5bApG0wTQPdK0TWyoSyWSsUCkU1xtZGx4g2wfy17QyvDm7Cg93royth+9maONrZMLRVEENbBZGQls3i/ReYvyea95cexdFOx63NtW3ybg18Sh9KVcNQK2uFQqGoBmTn5pGSqddqMldSziVl4OVih5tj+ThlVTUqTdUtIcSvQohtQojXy3NehUKhqO442NpUakUNUMfHWSnqUlJuyloIMRqwkVJ2AeoLIcLKa26FQqFQKKoy5bmy7gXMNr5fBXQveFII8bAQIkIIEZGQYJ0SawqFQqFQVEXKU1m7ADHG98nAdal2pJRTpZThUspwPz+/chRLoVAoFIrKTXkq63QgPx+daznPrVAoFApFlaU8FeZurm19twbOlOPcCoVCoVBUWcozznohsEkIEQQMAjqX49wKhUKhUFRZym1lLaVMRXMy2w70llKmlNfcCoVCoVBUZSplUhQhRAJw1sLD+gKJFh6zIqgu1wHqWior1eVaqst1gLqWyoqlryVUSlmoh3WlVNbWQAgRUVRmmKpEdbkOUNdSWaku11JdrgPUtVRWyvNalEe2QqFQKBSVHKWsFQqFQqGo5NQkZT21ogWwENXlOkBdS2WlulxLdbkOUNdSWSm3a6kxNmuFQqFQKKoqNWllrVAoFApFlUQpa4WiFAghvIUQ/YUQvhUti0KhqP4oZV3JEUJ4CCGWCyFWCSEWCCHshRDnhBD/GV8tK1pGc6gOSk4I4QX8C3QE1gsh/KryPalOCCH8hRB7hRC26p5UPIXdB3VfSke1tFkLITyAmYANcAUYD/wANAOWSinfr0DxzEII8ThwUkq5WgjxAxALuEgpX6pg0czGqOSWGl8TgD7AR1Sx+yKEuAXIllJuF0JMARIA76p4T/IRQvgDK6SUbYUQv1LF7kk+QohpQAfgDmB8VbwnQghbIMr4AngSeIYqeE+EEO0ocB9u/FwVEUJ8DyyXUi4pz/+V6rqyvhP4XEo5AIhDUww2UsouQH0hRFiFSmcGUsrvpZSrjR/9gFxgqBBipxDiV+M/dlWhFTBZSvkBsBJNWVe5+yKl3GBU1D3RVteZVN17ks8UwEkIMZoqeE8AhBB90B7O49BqD1TVe9IKmCGl7CWl7AWEUUXvCTfcB7RiTlX1viCE6AEEGBV1uf6vVEtlXYiCuwuYbfy8imvVv6oMQogugBewGugnpewI2AGDK1QwMyhEyd1KFb0vQgiBtmNzCdhLFb0ncJOS60UVvCdCCHvgDeBl46FdVN17cqOC60cVvCdGbrwPXlTR+yKEsAN+Bs4IIUZQzv8r1VJZ51NAwZ0HYoyHkwH/ChOqFAghvIFvgPuBA1LKWOOpCLSn7irDDUpOUkXvi9R4AjgABFXVe1KIknOhat6Tl4HvpZSXjZ+r8v/JjQpuEFXznsDN9yGrCt+XicAR4BO0xcYTlON9qbbK+gYFlw44GU+5UoWu2/hlOgd4RUp5FpgmhGgthLABRgL7K1I+c7lByXWlCt4XIcRLQoiJxo+ewI9V+J7cqOSq6v9KP+AJIcR/QBtgeRW+JzcqOF+q5j2Bm7+vXqrC96UtMFVKGQf8DWykHO9LVbrpJlOIgtvNtS2K1sCZChKtNDwAtANeM34RHQamAfuAbVLKNRUnmnkUouQ+omrel6nA3UKIjWhOjD2poveEm5XcMKrgPZFS9ixg490HPEXVvSc3KrgnqIL3xMi7FLgPVO3/lUigvvF9OFCXcrwv1dUb/DHgQ649tf0OTAbWom0pdf5/e3cTYlUZx3H8+9NixvIFlIEKpRcFiSzdFQhBVFKLXiDa9IKI0OAmIjEwQbIanBa6cpkhJPYmFGaBBBaFUgt3tXBlUNSi3BQk0Yy/Fs+THMZ7Z3QaxnNmfh+4zDnPec5z7+Fh+N9z7jn/f+ppz756N/iHwADwPbCT8u0089ICNWA/DnxD5uSakbQOOAIIOAa8RebkmpO0BHiHcrn7esqNy8eYpXmZk8G6lxooHga+rpcxogUyL+2TOWmfzEk7zea8zJtgHRER0VVz8jfriIiIuSTBOiIiouUSrCM6SNKApNVXuc+iqXtFRBslWEd00zOUPAI9SVpZC6YMN5oPSnrkSgaXdKukfY31B2rOfSQ9JOmNxrZHG8t7O5YOM6ITEqwjOkbSEPAmsFjScUln6t/PJX1Zu+2n5JF/UdKIpBuAQcrz4ZP+39e++4GRRvMwsLwuj9fXf15tLI8Co5KWTvf4IuJyuRs8okNqfuLPgJO2R2vbt7bva/QZolSZOwKsomRbGqbkMP8beAp4yfY/fd5jB3DK9ulG22uUXMiifMk3cLFu/s32042+64Enbe+ZgUOOCBKsIzpF0m2UFLpLgTW1eSMlO9R1lCQNPwB3A7cAf1DOsNcDXwHnKcF20PbhPu/xru3n+2zbAtxp+5UpPmffMSLi6nWqPFnEfGf7R2C3pJPAJttj9cz6UvUiSXcAD1KyLH0KbAMOAUuA2ym5p49O8jZjvRol3QPsBn6u2c6gFP44a/u5KxkjIqYnwTqiQ2q+aCiXoXttX0C5VH2Rkr/4HCWg/ln3uZFyGfyLXvtXY5KWNVMnStoI7AM2Az/ZPlfbDwHvTfgMi+pniIgZkmAd0S0vAE8AF4BPSsVR1ko6XrcvBD4GNlEKJgxSgvQyyg1i91NyGk/mMKUa185G20pKUYkh4CNJr1OKGgzYPjFh/+3AB9M4tojoI79ZR3ScpO9s39tYX00p/nAKuAm4GdgCHAV+qSVKpxpzBPjV9oFG2wJKpaHHgL2UR8d22R5r9NkK3GX75Rk4tIiocmYd0X2LJ6z/TrkT/CylbN/btsclPQuckLTC9vnJBrS9S9IGgPrc9PvAX3XM08CqPmOcsX3w/xxMRFwuZ9YR84ikhbbHp+4ZEW2SYB0REdFyyWAWERHRcgnWERERLZdgHRER0XIJ1hERES33L+4cYN3G4HpsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.sans-serif'] = 'SimHei'\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "plt.figure(figsize=(8, 4))\n",
    "sns.lineplot(data=df)\n",
    "plt.xlabel(\"年龄（岁）\")\n",
    "plt.ylabel(\"人数\")\n",
    "plt.title(\"年龄与客户流失人数的关系\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 7],\n",
       "       [24],\n",
       "       [17],\n",
       "       [13],\n",
       "       [15],\n",
       "       [11],\n",
       "       [22],\n",
       "       [18],\n",
       "       [11],\n",
       "       [20],\n",
       "       [14],\n",
       "       [14],\n",
       "       [14],\n",
       "       [16],\n",
       "       [19],\n",
       "       [14],\n",
       "       [18],\n",
       "       [11],\n",
       "       [16],\n",
       "       [20],\n",
       "       [19],\n",
       "       [14],\n",
       "       [14],\n",
       "       [ 7],\n",
       "       [14],\n",
       "       [16],\n",
       "       [ 9],\n",
       "       [13],\n",
       "       [10],\n",
       "       [13],\n",
       "       [ 9],\n",
       "       [ 1],\n",
       "       [ 3],\n",
       "       [ 3],\n",
       "       [ 2],\n",
       "       [ 1],\n",
       "       [ 2],\n",
       "       [ 4],\n",
       "       [ 3],\n",
       "       [ 6],\n",
       "       [ 5]], dtype=int64)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values[:, [0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '年龄与客户流失人数的关系')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(df.index, df.values[:, 0], label=\"已流失\")\n",
    "plt.plot(df.index, df.values[:, 1], label=\"非流失\")\n",
    "plt.plot(df.index, df.values[:, 2], label=\"准流失\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"年龄（岁）\")\n",
    "plt.ylabel(\"人数\")\n",
    "plt.title(\"年龄与客户流失人数的关系\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 统计男性和女性客户的流失数量\n",
    "组合1行2列统计图，绘制男性客户流失柱状图和女性客户流失柱状图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[660],\n",
       "       [452],\n",
       "       [278]], dtype=int64)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 统计男性客户中几种类别的数量\n",
    "count1 = users.loc[users['SEX']==\"男\"]['TYPE'].value_counts()\n",
    "count1 = pd.DataFrame(count1)\n",
    "count1.columns = [\"数量（人）\"]\n",
    "count1\n",
    "count1.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>数量（人）</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>准流失</th>\n",
       "      <td>485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>非流失</th>\n",
       "      <td>352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>已流失</th>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     数量（人）\n",
       "准流失    485\n",
       "非流失    352\n",
       "已流失    204"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 统计女性客户中几种类别的数量\n",
    "count2 = users[users['SEX']=='女']['TYPE'].value_counts()\n",
    "count2 = pd.DataFrame(count2)\n",
    "count2.columns=[\"数量（人）\"]\n",
    "count2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['准流失', '非流失', '已流失'], dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index1 = count1.index\n",
    "index1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['准流失', '非流失', '已流失'], dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index2 = count2.index\n",
    "index2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '女性')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(8, 4))\n",
    "sns.barplot(x=index1, y=count1['数量（人）'], ax=axes[0])\n",
    "axes[0].set_title(\"男性\")\n",
    "sns.barplot(x=index2, y=count2['数量（人）'], ax=axes[1])\n",
    "axes[1].set_title(\"女性\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 统计订单详情表重复数量\n",
    "使用duplicated()方法统计订单详情表重复数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "订单详情表重复个数： 0\n"
     ]
    }
   ],
   "source": [
    "print(\"订单详情表重复个数：\", info.duplicated(subset=['name', 'use_start_time']).sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 去除订单详情表中的异常值\n",
    "排除同一时间桌子被不同人使用的订单"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['info_id', 'emp_id', 'number_consumers', 'mode', 'dining_table_id',\n",
       "       'dining_table_name', 'expenditure', 'dishes_count', 'accounts_payable',\n",
       "       'use_start_time', 'check_closed', 'lock_time', 'cashier_id', 'pc_id',\n",
       "       'order_number', 'org_id', 'print_doc_bill_num', 'lock_table_info',\n",
       "       'order_status', 'phone', 'name'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind = info[info.duplicated(subset=['dining_table_id', 'use_start_time'])].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "同一时间桌子被不同人使用的订单：\n",
      "       info_id  dining_table_id      use_start_time\n",
      "2052     3392             1484 2016-03-26 21:55:00\n",
      "2140     3480             1484 2016-03-26 21:55:00\n"
     ]
    }
   ],
   "source": [
    "print('同一时间桌子被不同人使用的订单：\\n',\n",
    "     info[(info['dining_table_id'] == info.iloc[ind[1], :]['dining_table_id']) & \n",
    "     (info['use_start_time'] == info.iloc[ind[1], :]['use_start_time'])]\n",
    "     [['info_id', 'dining_table_id','use_start_time']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>info_id</th>\n",
       "      <th>emp_id</th>\n",
       "      <th>number_consumers</th>\n",
       "      <th>mode</th>\n",
       "      <th>dining_table_id</th>\n",
       "      <th>dining_table_name</th>\n",
       "      <th>expenditure</th>\n",
       "      <th>dishes_count</th>\n",
       "      <th>accounts_payable</th>\n",
       "      <th>use_start_time</th>\n",
       "      <th>...</th>\n",
       "      <th>lock_time</th>\n",
       "      <th>cashier_id</th>\n",
       "      <th>pc_id</th>\n",
       "      <th>order_number</th>\n",
       "      <th>org_id</th>\n",
       "      <th>print_doc_bill_num</th>\n",
       "      <th>lock_table_info</th>\n",
       "      <th>order_status</th>\n",
       "      <th>phone</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1326</td>\n",
       "      <td>3556</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1485</td>\n",
       "      <td>1006</td>\n",
       "      <td>423</td>\n",
       "      <td>13</td>\n",
       "      <td>423</td>\n",
       "      <td>2016-02-05 19:08:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-02-05 19:15:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>18688882708</td>\n",
       "      <td>麻庶汐</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1327</td>\n",
       "      <td>1874</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1516</td>\n",
       "      <td>1038</td>\n",
       "      <td>1101</td>\n",
       "      <td>29</td>\n",
       "      <td>1101</td>\n",
       "      <td>2016-01-04 11:51:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-01-04 12:09:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>18688881026</td>\n",
       "      <td>濮明智</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1328</td>\n",
       "      <td>3484</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1504</td>\n",
       "      <td>1010</td>\n",
       "      <td>437</td>\n",
       "      <td>20</td>\n",
       "      <td>437</td>\n",
       "      <td>2016-01-29 13:31:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-01-29 13:37:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>18688882636</td>\n",
       "      <td>姜萌萌</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1329</td>\n",
       "      <td>3639</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1482</td>\n",
       "      <td>1003</td>\n",
       "      <td>251</td>\n",
       "      <td>8</td>\n",
       "      <td>251</td>\n",
       "      <td>2016-01-19 12:02:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-01-19 12:14:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>18688882791</td>\n",
       "      <td>封振翔</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1330</td>\n",
       "      <td>3835</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1480</td>\n",
       "      <td>1002</td>\n",
       "      <td>363</td>\n",
       "      <td>6</td>\n",
       "      <td>363</td>\n",
       "      <td>2016-07-18 12:35:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-07-18 12:45:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>18688882987</td>\n",
       "      <td>白子晨</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6606</th>\n",
       "      <td>7971</td>\n",
       "      <td>2442</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1511</td>\n",
       "      <td>1023</td>\n",
       "      <td>710</td>\n",
       "      <td>16</td>\n",
       "      <td>710</td>\n",
       "      <td>2016-07-12 11:19:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-07-12 11:26:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>18688881594</td>\n",
       "      <td>栾丽萍</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6607</th>\n",
       "      <td>7972</td>\n",
       "      <td>3147</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1509</td>\n",
       "      <td>1016</td>\n",
       "      <td>1318</td>\n",
       "      <td>46</td>\n",
       "      <td>1318</td>\n",
       "      <td>2016-07-21 12:50:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-07-21 12:58:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>18688882299</td>\n",
       "      <td>陈仲锋</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6608</th>\n",
       "      <td>7973</td>\n",
       "      <td>1293</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1482</td>\n",
       "      <td>1002</td>\n",
       "      <td>346</td>\n",
       "      <td>6</td>\n",
       "      <td>346</td>\n",
       "      <td>2016-01-20 19:15:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-01-20 19:28:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>18688880492</td>\n",
       "      <td>蒲水莲</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6609</th>\n",
       "      <td>7974</td>\n",
       "      <td>1947</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1492</td>\n",
       "      <td>1015</td>\n",
       "      <td>1401</td>\n",
       "      <td>41</td>\n",
       "      <td>1401</td>\n",
       "      <td>2016-03-28 19:50:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-03-28 19:57:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>18688881099</td>\n",
       "      <td>温伟超</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6610</th>\n",
       "      <td>7975</td>\n",
       "      <td>2276</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1485</td>\n",
       "      <td>1033</td>\n",
       "      <td>301</td>\n",
       "      <td>13</td>\n",
       "      <td>301</td>\n",
       "      <td>2016-06-16 12:55:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2016-06-16 13:14:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>330</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>18688881428</td>\n",
       "      <td>经子颍</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6594 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      info_id  emp_id  number_consumers  mode  dining_table_id  \\\n",
       "0        1326    3556                 4   NaN             1485   \n",
       "1        1327    1874                 7   NaN             1516   \n",
       "2        1328    3484                 5   NaN             1504   \n",
       "3        1329    3639                 2   NaN             1482   \n",
       "4        1330    3835                 2   NaN             1480   \n",
       "...       ...     ...               ...   ...              ...   \n",
       "6606     7971    2442                 4   NaN             1511   \n",
       "6607     7972    3147                10   NaN             1509   \n",
       "6608     7973    1293                 2   NaN             1482   \n",
       "6609     7974    1947                 8   NaN             1492   \n",
       "6610     7975    2276                 3   NaN             1485   \n",
       "\n",
       "      dining_table_name  expenditure  dishes_count  accounts_payable  \\\n",
       "0                  1006          423            13               423   \n",
       "1                  1038         1101            29              1101   \n",
       "2                  1010          437            20               437   \n",
       "3                  1003          251             8               251   \n",
       "4                  1002          363             6               363   \n",
       "...                 ...          ...           ...               ...   \n",
       "6606               1023          710            16               710   \n",
       "6607               1016         1318            46              1318   \n",
       "6608               1002          346             6               346   \n",
       "6609               1015         1401            41              1401   \n",
       "6610               1033          301            13               301   \n",
       "\n",
       "          use_start_time  ...           lock_time cashier_id  pc_id  \\\n",
       "0    2016-02-05 19:08:00  ... 2016-02-05 19:15:00        NaN    NaN   \n",
       "1    2016-01-04 11:51:00  ... 2016-01-04 12:09:00        NaN    NaN   \n",
       "2    2016-01-29 13:31:00  ... 2016-01-29 13:37:00        NaN    NaN   \n",
       "3    2016-01-19 12:02:00  ... 2016-01-19 12:14:00        NaN    NaN   \n",
       "4    2016-07-18 12:35:00  ... 2016-07-18 12:45:00        NaN    NaN   \n",
       "...                  ...  ...                 ...        ...    ...   \n",
       "6606 2016-07-12 11:19:00  ... 2016-07-12 11:26:00        NaN    NaN   \n",
       "6607 2016-07-21 12:50:00  ... 2016-07-21 12:58:00        NaN    NaN   \n",
       "6608 2016-01-20 19:15:00  ... 2016-01-20 19:28:00        NaN    NaN   \n",
       "6609 2016-03-28 19:50:00  ... 2016-03-28 19:57:00        NaN    NaN   \n",
       "6610 2016-06-16 12:55:00  ... 2016-06-16 13:14:00        NaN    NaN   \n",
       "\n",
       "      order_number  org_id  print_doc_bill_num  lock_table_info  order_status  \\\n",
       "0              NaN     330                 NaN              NaN             1   \n",
       "1              NaN     330                 NaN              NaN             1   \n",
       "2              NaN     330                 NaN              NaN             1   \n",
       "3              NaN     330                 NaN              NaN             1   \n",
       "4              NaN     330                 NaN              NaN             1   \n",
       "...            ...     ...                 ...              ...           ...   \n",
       "6606           NaN     330                 NaN              NaN             1   \n",
       "6607           NaN     330                 NaN              NaN             1   \n",
       "6608           NaN     330                 NaN              NaN             1   \n",
       "6609           NaN     330                 NaN              NaN             1   \n",
       "6610           NaN     330                 NaN              NaN             1   \n",
       "\n",
       "            phone  name  \n",
       "0     18688882708   麻庶汐  \n",
       "1     18688881026   濮明智  \n",
       "2     18688882636   姜萌萌  \n",
       "3     18688882791   封振翔  \n",
       "4     18688882987   白子晨  \n",
       "...           ...   ...  \n",
       "6606  18688881594   栾丽萍  \n",
       "6607  18688882299   陈仲锋  \n",
       "6608  18688880492   蒲水莲  \n",
       "6609  18688881099   温伟超  \n",
       "6610  18688881428   经子颍  \n",
       "\n",
       "[6594 rows x 21 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info.drop(index=ind, inplace=True)\n",
    "info.reset_index(drop=True)\n",
    "info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "异常值个数： 17\n",
      "去除异常值订单详情表维数： (6594, 21)\n"
     ]
    }
   ],
   "source": [
    "print('异常值个数：', len(ind))\n",
    "print('去除异常值订单详情表维数：', info.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 统计缺失值数量\n",
    "1.统计客户信息表缺失值个数；2.订单详情表缺失值个数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "客户信息表缺失值个数： 46158\n",
      "订单详情表缺失值个数： 50842\n"
     ]
    }
   ],
   "source": [
    "print('客户信息表缺失值个数：', info.isnull().sum().sum())\n",
    "print('订单详情表缺失值个数：', users.isnull().sum().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 合并两个表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "positional indexers are out-of-bounds",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-25-316affee3030>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# 获取最后一次用餐时间\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0musers\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[0minfo1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minfo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0minfo\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0minfo\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'name'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0musers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minfo\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'name'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0musers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m         \u001b[0musers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m14\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minfo1\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'use_start_time'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    871\u001b[0m                     \u001b[1;31m# AttributeError for IntervalTree get_value\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    872\u001b[0m                     \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 873\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    874\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    875\u001b[0m             \u001b[1;31m# we by definition only have the 0th axis\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_tuple\u001b[1;34m(self, tup)\u001b[0m\n\u001b[0;32m   1441\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mTuple\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1442\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1443\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_has_valid_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1444\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1445\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_lowerdim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_has_valid_tuple\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    700\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Too many indexers\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    701\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 702\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_key\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    703\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    704\u001b[0m                 raise ValueError(\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_validate_key\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1365\u001b[0m             \u001b[1;31m# check that the key does not exceed the maximum size of the index\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1366\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0mlen_axis\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;33m-\u001b[0m\u001b[0mlen_axis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1367\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"positional indexers are out-of-bounds\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1368\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1369\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"Can only index by location with a [{self._valid_types}]\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: positional indexers are out-of-bounds"
     ]
    }
   ],
   "source": [
    "# 获取最后一次用餐时间\n",
    "for i in range(len(users)):\n",
    "    info1 = info.iloc[info[info['name'] == users.iloc[i, 2]].index.tolist(), :]\n",
    "    if sum(info['name'] == users.iloc[i, 2]) != 0:\n",
    "        users.iloc[i, 14] = max(info1['use_start_time'])\n",
    "# 获取订单状态为1的订单\n",
    "info = info.loc[info['order_status'] == 1, ['emp_id', 'number_consumers', 'expenditure']]\n",
    "info = info.rename(columns={'emp_id': 'USER_ID'})  # 修改列名\n",
    "user = users[['USER_ID', 'LAST_VISITS', 'TYPE']]\n",
    "\n",
    "# 合并两个表\n",
    "info_user = pd.merge(user, info, left_on='USER_ID', right_on='USER_ID', how='left')\n",
    "print('合并表缺失值个数：\\n', info_user.isnull().sum())\n",
    "info_user.dropna(inplace=True)\n",
    "\n",
    "info_user.to_csv('../tmp/info_user.csv', index=False, encoding='utf-8')\n",
    "\n",
    "print('处理缺失值数据维度：\\n', info_user.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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